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Erasable-itemset mining is a valuable method of pattern extraction for helping the manager of a factory analyze production planning. The erasable itemsets derived can be considered important production information regarding how to plan the production of a factory during an economic depression or financial shortage for the manager. After the erasable-itemset mining was proposed in 2009, several efficient mining approaches for finding erasable itemsets have been developed. However, these methods require a considerable amount of execution time when the amount of product data is large. Especially, manufacturing small amounts of versatile products has been a trend today, and it will generate a large product database. This paper adopts a bitmap representation for itemsets in an erasable-itemset mining algorithm to speed up the execution. Unlike the traditional bitmap meaning for frequent itemsets, a bitmap for erasable-itemset mining here denotes the relationship that a product includes at least one material (item) in a specified itemset. Using the bitmap representation can easily find the desired products to check, thus decreasing the scans of a database. Experimental evaluation on synthesized and real datasets was used to compare the proposed approach with the other two under different parameter values. The experimental results show that the proposed approach can make a good trade-off between execution time and memory usage.

In recent years, fuzzy utility mining has become an area of interest due to advancement of human reasoning. With regards to real applications, transactions in a database often involve things, such as transaction time, stamp, and much more. It is also noted that not all products in a store are displayed on the shelf, especially the seasonal ones. This paper, therefore, addresses these issues by presenting an effective framework called temporal-based fuzzy utility mining to give more attention to the transaction period of given items according to the concept of fuzzy utility mining. The temporal-based fuzzy utility mining proposed here is, however, a more complex approach when compared with the traditional fuzzy utility mining. A more complicated model for non-lost upper-bound fuzzy utility is thus proposed for effective mining. Furthermore, based on this model, a two-phase algorithm is developed for temporal-based fuzzy utility mining. Finally, the difference of fuzzy utility item sets with and without consideration of the lifetime of the items is shown by the experimental results under various experimental conditions.

Many of the strongest game playing programs use a combination of Monte Carlo tree search (MCTS) and deep neural networks (DNN), where the DNNs are used as policy or value evaluators. Given a limited budget, such as online playing or during the self-play phase of AlphaZero (AZ) training, a balance needs to be reached between accurate state estimation and more MCTS simulations, both of which are critical for a strong game playing agent. Typically, larger DNNs are better at generalization and accurate evaluation, while smaller DNNs are less costly, and therefore can lead to more MCTS simulations and bigger search trees with the same budget. This paper introduces a new method called the multiple policy value MCTS (MPV-MCTS), which combines multiple policy value neural networks (PV-NNs) of various sizes to retain advantages of each network, where two PV-NNs f_S and f_L are used in this paper. We show through experiments on the game NoGo that a combined f_S and f_L MPV-MCTS outperforms single PV-NN with policy value MCTS, called PV-MCTS. Additionally, MPV-MCTS also outperforms PV-MCTS for AZ training., Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19)

Most of the existing studies in temporal data mining consider only lifespan of items to find general temporal association rules. However, an infrequent item for the entire time may be frequent within part of the time. We thus organize time into granules and consider temporal data mining for different levels of granules. Besides, an item may not be ready at the beginning of a store. In this paper, we use the first transaction including an item as the start point for the item. Before the start point, the item may not be brought. A three-phase mining framework with consideration of the item lifespan definition is designed. At last, experiments were made to demonstrate the performance of the proposed framework.

We propose a fuzzy temporal association rule mining algorithm (FTARM).Information inside transactions can be found correctly by using lifespan of items.Three datasets are used to show the FTARM is effective.Experiments show that FTARM can derive more rules than FAR.The derived rules are better than FAR in terms of supports and confidences. Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In real-world applications, transactions may contain quantitative values and each item may have a lifespan from a temporal database. In this paper, we thus propose a data mining algorithm for deriving fuzzy temporal association rules. It first transforms each quantitative value into a fuzzy set using the given membership functions. Meanwhile, item lifespans are collected and recorded in a temporal information table through a transformation process. The algorithm then calculates the scalar cardinality of each linguistic term of each item. A mining process based on fuzzy counts and item lifespans is then performed to find fuzzy temporal association rules. Experiments are finally performed on two simulation datasets and the foodmart dataset to show the effectiveness and the efficiency of the proposed approach.

We introduce a new fuzzy utility function to evaluate the fuzzy utilities of itemsets.The fuzzy minimum operator is applied to the proposed fuzzy utility function.An effective model is designed to keep downward-closure property.A two-phase fuzzy utility mining algorithm, namely TPFU, is proposed.The experiments in synthetic and real dataset prove the practicality of TPFU. Fuzzy utility mining has been an emerging research issue because of its simplicity and comprehensibility. Different from traditional fuzzy data mining, fuzzy utility mining considers not only quantities of items in transactions but also their profits for deriving high fuzzy utility itemsets. In this paper, we introduce a new fuzzy utility measure with the fuzzy minimum operator to evaluate the fuzzy utilities of itemsets. Besides, an effective fuzzy utility upper-bound model based on the proposed measure is designed to provide the downward-closure property in fuzzy sets, thus reducing the search space of finding high fuzzy utility itemsets. A two-phase fuzzy utility mining algorithm, named TPFU, is also proposed and described for solving the problem of fuzzy utility mining. At last, the experimental results on both synthetic and real datasets show that the proposed algorithm has good performance.

Weighted itemset mining has been a widely studied topic in data mining. The reason is that weighted itemset mining considers not only the occurrence of items in transactions but also the individual importance of items. The traditional upper-bound model can be used to handle the weighted itemset mining problem, but a large number of candidates are generated by the model. This work thus presents an improved model to enhance the effectiveness of reducing unpromising candidates. Besides, an effective strategy, projection-based pruning, is proposed as well to tighten upper-bounds of weighted supports for itemsets in the mining process, thus reducing the execution time further. Through a series of experimental evaluation, the results on synthetic and real datasets show that the proposed approach has good performance in both pruning effectiveness and execution efficiency under various parameter settings when compared to some other approaches.

Association-rule mining is used to mine the relationships among the occurrences itemsets in a transactional database. An item is treated as a binary variable whose value is one if it appears in a transaction and zero otherwise. In real-world appli- cations, several products may be purchased at the same time, with each product having an associated profit, quantity, and price. Association-rule mining from a binary database is thus not sufficient in some applications. Utility mining was thus proposed as an extension of frequent-itemset mining for considering various factors from the user. Most utility mining approaches can only process static databases and use batch processing. In real-world applications, transactions are dynamically inserted into or deleted from databases. The Fast UPdated (FUP) algorithm and the FUP2 algorithm were respectively proposed to handle trans- action insertion and deletion in dynamic databases. In this paper, a fast-updated high-utility itemsets for transaction deletion (FUP-HUI-DEL) algorithm is proposed to handle transaction deletion for efficiently updating discovered high utility itemsets in decremental mining. The two-phase approach in high utility mining is applied to the proposed FUP-HUI-DEL algorithm for preserving the downward closure property to reduce the number of candidates. The FUP2 algorithm for handling transaction deletion in association-rule mining is adopted in the proposed FUP-HUI-DEL algorithm to reduce the number of scans of the original database in high utility mining. Experiments show that the proposed FUP-HUI-DEL algorithm outperforms the batch two-phase approach.

This paper proposes a bitmap representation approach for modifying the erasable-itemset mining algorithm to increase its efficiency. The proposed approach uses the bitmap concept to save processing time. Through experimental evaluation, simulation datasets were used to compare the traditional erasable itemset mining and the proposed approach under various experimental conditions.

Mining useful information from large databases has become an important research issue in recent years. Among the classes of derived knowledge, sequential patterns can be used to discover customer behaviors to facilitate efficient decision-making. The fast updated frequent-pattern (FUFP)-tree algorithm has been proposed to efficiently mine frequent itemsets through transaction insertion in incremental mining. The present study extends this algorithm to develop an incremental fast updated sequential-pattern (FUSP)-tree algorithm for efficiently handling sequential patterns whether the newly inserted transactions are from customers already in the database or from new customers. An FUSP-tree structure is designed to make the update process easier. The FUSP-growth algorithm is proposed for mining frequent sequences (sequential patterns) from the FUSP-tree. Experimental results show that the proposed FUSP-tree algorithm has good performance for incrementally handling newly inserted transactions.

Recently, high utility sequential pattern mining has been an emerging popular issue due to the consideration of quantities, profits and time orders of items. The utilities of subsequences in sequences in the existing approach are difficult to be calculated due to the three kinds of utility calculations. To simplify the utility calculation, this work then presents a maximum utility measure, which is derived from the principle of traditional sequential pattern mining that the count of a subsequence in the sequence is only regarded as one. Hence, the maximum measure is properly used to simplify the utility calculation for subsequences in mining. Meanwhile, an effective upper-bound model is designed to avoid information losing in mining, and also an effective projection-based pruning strategy is designed as well to cause more accurate sequence-utility upper-bounds of subsequences. The indexing strategy is also developed to quickly find the relevant sequences for prefixes in mining, and thus unnecessary search time can be reduced. Finally, the experimental results on several datasets show the proposed approach has good performance in both pruning effectiveness and execution efficiency.

We introduce a new research work, on-shelf utility mining with negative item values.We propose a TS-HOUN algorithm for mining the new type of utility itemsets.The derived itemsets are expected to be more reliable in terms of business.The synthetic and real datasets are used to show the TS-HOUN is effective.Experiments show that TS-HOUN can find more utility itemsets with negative values. On-shelf utility mining has recently received interest in the data mining field due to its practical considerations. On-shelf utility mining considers not only profits and quantities of items in transactions but also their on-shelf time periods in stores. Profit values of items in traditional on-shelf utility mining are considered as being positive. However, in real-world applications, items may be associated with negative profit values. This paper proposes an efficient three-scan mining approach to efficiently find high on-shelf utility itemsets with negative profit values from temporal databases. In particular, an effective itemset generation method is developed to avoid generating a large number of redundant candidates and to effectively reduce the number of data scans in mining. Experimental results for several synthetic and real datasets show that the proposed approach has good performance in pruning effectiveness and execution efficiency.

Weighted sequential pattern mining has recently been discussed in the field of data mining. Different from traditional sequential pattern mining, this kind of mining considers different significances of items in real applications, such as cost or profit. Most of the related studies adopt the maximum weighted upper-bound model to find weighted sequential patterns, but they generate a large number of unpromising candidate subsequences. In this study, we thus propose an efficient approach for finding weighted sequential patterns from sequence databases. In particular, a tightening strategy in the proposed approach is proposed to obtain more accurate weighted upper-bounds for subsequences in mining. Through the experimental evaluation, the results also show the proposed approach has good performance in terms of pruning effectiveness and execution efficiency.

Partial periodic patterns are commonly seen in real-world applications. The major problem of mining partial periodic patterns is the efficiency problem due to a huge set of partial periodic candidates. Although some efficient algorithms have been developed to tackle the problem, the performance of the algorithms significantly drops when the mining parameters are set low. In the past, the authors have adopted the projection-based approach to discover the partial periodic patterns from single-event time series. In this paper, the authors extend it to mine partial periodic patterns from a sequence of event sets which multiple events concurrently occur at the same time stamp. Besides, an efficient pruning and filtering strategy is also proposed to speed up the mining process. Finally, the experimental results on a synthetic dataset and real oil price dataset show the good performance of the proposed approach.

Partial periodic pattern mining has recently become an important issue in the field of data mining due to its wide applications in many businesses. A partial periodic pattern considers part of but not all the events within a specific period length, repeating with high frequency in an event sequence. Traditional partial periodic pattern mining, however, only considered the frequencies of patterns, but did not consider events that might have different importance. The study thus proposes a weighted partial periodic patterns mining algorithm to resolve this problem. To increase the efficiency, the two-phase upper-bound weighted model based on segmental maximum weights is adopted to prune unimportant candidates in early stage. Then the weighted partial periodic patterns are discovered from the candidate patterns. Finally, the experimental results on synthetic datasets and a real oil dataset show that the weighted partial periodic pattern mining is more practical to assist users for decision making.

Data mining has been studied for a long time. Its goal is to help market managers find relationships among items from large databases and thus increase sales volume. Association-rule mining is one of the well known and commonly used techniques for this purpose. The Apriori algorithm is an important method for such a task. Based on the Apriori algorithm, lots of mining approaches have been proposed for diverse applications. Many of these data mining approaches focus on positive association rules such as ''if milk is bought, then cookies are bought''. Such rules may, however, be misleading since there may be customers that buy milk and not buy cookies. This paper thus takes the properties of propositional logic into consideration and proposes an algorithm for mining highly coherent rules. The derived association rules are expected to be more meanful and reliable for business. Experiments on two datasets are also made to show the performance of the proposed approach.

In traditional association rule mining, most algorithms are designed to discover frequent itemsets from a binary database. Utility mining was thus proposed to measure the utility values of purchased items for revealing high utility itemsets from a quantitative database. In the past, a two-phase high utility mining algorithm was thus proposed for efficiently discovering high utility itemsets from a quantitative database. In dynamic data mining, transactions may be inserted, deleted, or modified from a database. In this case, a batch mining procedure must rescan the whole updated database to maintain the up-to-date information. Designing an efficient approach for handling dynamic databases is thus a critical research issue in utility mining. In this paper, an incremental mining algorithm is proposed for efficiently maintaining discovered high utility itemsets based on pre-large concepts. Itemsets are first partitioned into three parts according to whether they have large (high), pre-large, or small transaction-weighted utilization in the original database and in inserted transactions. Individual procedures are then executed for each part. Experimental results show that the proposed incremental high utility mining algorithm outperforms existing algorithms.

Recently, utility mining has widely been discussed in the field of data mining. It finds high utility itemsets by considering both profits and quantities of items in transactional data sets. However, most of the existing approaches are based on the principle of levelwise processing, as in the traditional two-phase utility mining algorithm to find a high utility itemsets. In this paper, we propose an efficient utility mining approach that adopts an indexing mechanism to speed up the execution and reduce the memory requirement in the mining process. The indexing mechanism can imitate the traditional projection algorithms to achieve the aim of projecting sub-databases for mining. In addition, a pruning strategy is also applied to reduce the number of unpromising itemsets in mining. Finally, the experimental results on synthetic data sets and on a real data set show the superior performance of the proposed approach.

Utility mining has recently been discussed in the field of data mining. A utility itemset considers both profits and quantities of items in transactions, and thus its utility value increases with increasing itemset length. To reveal a better utility effect, an average-utility measure, which is the total utility of an itemset divided by its itemset length, is proposed. However, existing approaches use the traditional average-utility upper-bound model to find high average-utility itemsets, and thus generate a large number of unpromising candidates in the mining process. The present study proposes an improved upper-bound approach that uses the prefix concept to create tighter upper bounds of average-utility values for itemsets, thus reducing the number of unpromising itemsets for mining. Results from experiments on two real databases show that the proposed algorithm outperforms other mining algorithms under various parameter settings.

Association-rule mining, which is based on frequency values of items, is the most common topic in data mining. In real-world applications, customers may, however, buy many copies of products and each product may have different factors, such as profits and prices. Only mining frequent itemsets in binary databases is thus not suitable for some applications. Utility mining is thus presented to consider additional measures, such as profits or costs according to user preference. In the past, a two-phase mining algorithm was designed for fast discovering high utility itemsets from databases. When data come intermittently, the approach needs to process all the transactions in a batch way. In this paper, an incremental mining algorithm for efficiently mining high utility itemsets is proposed to handle the above situation. It is based on the concept of the fast-update (FUP) approach, which was originally designed for association mining. The proposed approach first partitions itemsets into four parts according to whether they are high transaction-weighted utilization itemsets in the original database and in the newly inserted transactions. Each part is then executed by its own procedure. Experimental results also show that the proposed algorithm executes faster than the two-phase batch mining algorithm in the intermittent data environment

Utility mining has recently been an emerging topic in the field of data mining. It finds out high utility itemsets by considering both the profits and quantities of items in transactions. It may have a bias if items are not always on shelf. In this paper, we thus design a new kind of patterns, named high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. We also propose a two-phased mining algorithm to effectively and efficiently discover high on-shelf utility itemsets. In the first phase, the possible candidate on-shelf utility itemsets within each time period are found level by level. In the second phase, the candidate on-shelf utility itemsets are further checked for their actual utility values by an additional database scan. At last, the experimental results on synthetic datasets also show the proposed approach has a good performance.

Keyword: CAT, BP neural network, Translation preference, Foreignizing translation, Domesticating translation. Abstract. The thesis focuses on the efficiency problem caused by the unordered reference version provided by CAT software when calling the translation memory database. The BP neural network is used to study the connection between the translator's background and his/her preference for the goal of providing translators with ordered reference version. The thesis first acquires the research datum of the translator's background and their preference of translation.And the datum are quantified by options. Then, based on the BP neural network, an analysis model telling the preference between foreignizing and domesticating translation will be set up .Furthermore, the neural network will be trained through the training data set. Finally, with the help of detected set, we can check the grading accuracy between the predicted results and the expected value. Analysis model of the preference between foreignizing translation and domesticating translation of translators based on BP neural network can better recognize the translator's foreignizing and domesticating translation preference.

It’s of great significance for wind power integration into grid to forecast wind power. Based on forecasting wind power by BP neural network, the article introduces global optimization algorithm, Imperialist Competitive Algorithm (ICA) to provide optimized initial weights of BP neural network. Thus, it can overcome the entrapment in local optical optimum of BP neural network. Compared with BP neural network, it is found that the performances of ICA-BP neural network, which are training, testing and forecasting of wind power, are much better.

A GA-based privacy preserving utility mining method is proposed to find appropriate transactions to be inserted into the database for hiding sensitive high utility itemsets. It maintains the low information loss while providing information to the data demanders and protects the high-risk information in the database. A flexible evaluation function with three factors is designed in the proposed approach to evaluate whether the processed transactions are required to be inserted. Three different weights are, respectively, assigned to the three factors according to users. Moreover, the downward closure property and the prelarge concept are adopted in the proposed approach to reduce the cost of rescanning database, thus speeding up the evaluation process of chromosomes.

According to multi-site relationship, this work presents a new issue named online multi-site utility mining, which considers not only quantities and profits of items in transactions but also online mining in a multi-site environment, to effectively address the distributed utility mining in multiple sites. In addition, an effective online framework, namely TP-OMU (Three-Phase Online Multi-site Utility mining algorithm), is proposed for coping with this problem, and the predicting strategy is designed to reduce the number of unpromising candidates by their utility upper-bounds in mining. Finally, the experimental results show TP-OMU has good efficiency in comparison with the traditional two-phase utility mining approach.

In our previous approach, we proposed an apriori-based algorithm for mining highly coherent association rules, and it is time-consuming. In this paper, we present an efficient mining approach, which is a projection-based technique, to speed up the execution of finding highly coherent association rules. In particular, an indexing mechanism is designed to help find relevant transactions quickly from a set of data, and a pruning strategy is proposed as well to prune unpromising candidate itemsets early in mining. The experimental results show that the proposed algorithm outperforms the traditional mining approach for a real dataset.

Different from existing utility mining techniques with a single minimum utility criterion, this work presents a new utility-based framework that allows users to specify different minimum utility thresholds to items according to the characteristics or importance of items. In particular, the viewpoint of minimum constraints in traditional minimum utility mining is extended to decide the proper criterion for an itemset in mining when its items have different criteria. In addition, an effective mining approach is proposed to cope with the problem of multi-criteria utility mining. The experimental results show the effectiveness of the proposed viewpoint and approach under different parameter settings.

In this paper, a GA-based privacy-preserving utility mining method is proposed to delete appropriate transactions for hiding sensitive high utility itemsets from a database. The downward closure property and the pre-large concepts are adopted in the proposed algorithm to reduce the cost of rescanning databases. Experiments are also conducted to evaluate the performance of the proposed approach in execution time and the amount of side-effects.

This work handles fuzzy utility sequential pattern mining, which applies the fuzzy set concept to find interesting high utility sequential patterns with linguistic meaning. In particular, a new fuzzy utility function is developed to evaluate the linguistic benefit of a pattern in a sequence, and an effective approach is developed to find high fuzzy utility sequential patterns from a quantitative sequence database. The experimental results show the proposed approach has good performance under various parameter settings.

In the past, an incremental algorithm for mining high utility itemsets was proposed to derive high utility itemsets in an incrementally inserted way. In real-world applications, transactions are not only inserted into but also deleted from a database. In this paper, a maintenance algorithm is thus proposed for reducing the execution time of maintaining high utility itemsets due to transaction deletion. Experimental results also show that the proposed maintenance algorithm runs much faster than the batch approach.

To further enhance the performance of finding weighted frequent item sets, this work presents an effective upper-bound model for reducing unpromising candidates in mining process. To achieve this goal, a projection-based pruning strategy based on our previously proposed model is developed to gradually tighten the upper-bound value for each transaction. The experimental results show that the proposed approach can achieve good performance in efficiency.

Different from the existing algorithms, this work presents an efficient projection-based algorithm to find fuzzy frequent itemsets in quantitative databases. In particular, the proposed algorithm adopts the two strategies, indexing and pruning, to effectively speed up the execution efficiency in mining. In the experimental evaluation, the results on a synthetic dataset show the proposed algorithm executes faster than our previous algorithm.

Different from the existing studies, this work presents a new kind of rules with the concept of a hierarchy of time granules, namely hierarchical temporal association rules. The lifespan of an item in a time granule is calculated from the publication time of the item to the end time in the time granule. A three-phase mining framework is proposed to effectively and efficiently find this kind of rules from a temporal database. The experimental results show the performance of the proposed algorithm under the item lifespan definition.

In the data mining area, partial periodic pattern mining has become an important issue in many business applications. Although weighted sequential pattern mining algorithms have been widely discussed, until now, there is no further discussion in the field of weighed partial periodic pattern mining. Thus, this work introduces a new research issue, named weighted partial periodic pattern mining, which considers the individual significances of events in an event sequence. In addition, a projection-based mining algorithm is presented to effectively handle the weighted partial periodic pattern mining problem. The experimental results show the proposed algorithm is efficient.

The goal of data mining is to help market managers find relationships among items from large data sets to increase sales volume. The Apriori algorithm is a method for association rule mining, a data mining technique. Although a lot of mining approaches have been proposed based on the Apriori algorithm, most focus on positive association rules, such as i§If milk is bought, then bread is boughti¨. However, such a rule may be misleading since customers that buy milk may not buy bread. In this paper, an algorithm for mining highly coherent rules that takes the properties of propositional logic into consideration is proposed. The derived association rules may thus be more thoughtful and reliable. Experiments are conducted on simulation data sets to demonstrate the performance of the proposed approach.

This paper proposes an efficient fuzzy mining approach, namely GDF, to speed up the execution efficiency of finding fuzzy frequent itemsets in a quantitative database. Unlike the existing approaches, the proposed GDF adopts the data-reduction strategy to effectively reduce the number of unpromising candidate fuzzy itemsets for mining. The experimental results on a synthetic dataset reveal that the proposed GDF approach could outperform the traditional fuzzy mining algorithm.

In this paper, we propose an efficient projection-based algorithm to discover high sequential utility patterns from quantitative sequence databases. An effective pruning strategy in the proposed algorithm is designed to tighten upper-bounds for subsequences in mining. By using the strategy, a large number of unpromising subsequences could be pruned to improve execution efficiency. Finally, the experimental results on synthetic datasets show the proposed algorithm outperforms the previously proposed algorithm under different parameter settings.

There has been a considerable increase in the population proportion of senior people among global population. Fall often happens among these senior people, which is also the leading cause of their death. A real-time fall detection system may help us to detect fall events among elderly people in time and reduce the overall casualty rate. This paper proposed a home-based, real-time fall detection system that not only can distinguish up to 4 different kinds of fall events (forward, backward, rightward and leftward), but is also portable, low-cost and with high accuracy rate. The system includes a real-time fall detection band, a home server, and GSM instant messaging function which can transfer fall alert, send emergency help messages.

In this paper, a maintenance algorithm for average-utility mining is proposed to update derived high average-utility itemsets in dynamic databases. It first calculates the count difference of modified itemsets and then partitions them into four parts according to whether they are high upper-bound average-utility itemsets in the original database and whether their count difference is positive or negative. Each part is then processed in its own way. Experimental results show the proposed maintenance algorithm runs faster than the two-phase approach for mining high average-utility itemsets in dynamic databases.

In this paper, we try to improve the performance of utility mining. We propose a new projection-based mining algorithm and embed two pruning strategies in it to efficiently find high utility itemsets in a database. By using the two designed strategies, a large number of unpromising itemsets can be pruned away at an early stage, and the data size could recursively be reduced to save the scan time. Finally, the experimental results on synthetic datasets show the proposed algorithm runs faster than the other utility mining algorithms under different parameter settings.

In this paper, we propose an efficient mining approach which adopts an indexing mechanism to speed up the execution efficiency and reduce the memory requirement in the utility mining process. Besides, a pruning strategy is designed to reduce the number of unpromising itemsets for mining. The experimental results also show the performance of the proposed approach is better than that of traditional two-phase utility mining algorithm under different parameters.

Traditionally, the utility of an itemset is the summation of the utilities of the itemset in transactions regardless of its length. The average utility measure was then proposed to reveal a better utility effect of combining several items than the utility measure. In this paper, a maintenance algorithm for high average-utility itemsets is proposed for reducing execution time when records are deleted. The proposed approach is designed based on the two-phase utility mining and the FUP (Fast UPdate) concept and can achieve a good performance especially when the knowledge changes only to a small extent.

In this paper, we handle a new kind of patterns named high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. We propose a three-scan mining approach to effectively and efficiently discover high on-shelf utility itemsets. The proposed approach adopts an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from transactions. The experimental results on synthetic datasets also show the proposed approach has a good performance.

In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We also propose a two-phased mining algorithm to discover high transaction-weighted utility itemsets. The experimental results on synthetic datasets show the proposed approach has a good performance.

In this paper, instead of proposing the fastest mining algorithm in the world, we present a new approach in mining association rules. We propose a new algorithm - GRA (Gradational Reduction Approach). It adopts three mechanisms to increase the performance of mining. First, GRA algorithm uses a hash based technique, Hash MAP, which is similar to Hash Table to increase the access efficiency. Second, GRA algorithm uses an infrequent itemsets filtering mechanism to avoid generating a great deal of infrequent sub-itemsets of transaction records. Third, in order to reduce the size of database, GRA algorithm uses gradational reduction mechanism which uses the frequent itemsets as the information of filtration mechanisms to erase the infrequent items from database at every phase. GRA algorithm can decrease a large number of non-frequent itemsets and increase the utility rate of memory.

As a high efficient graphical soft development environment, LabVIEW is used in data acquiring and testing and measuring extensively. Because NI (national instruments) company doesn't support drivers for other company's card, there is an obstacle in programming by labVIEW in PC/104 system. By analyzing the mechanism of dynamic link library (DLL), this paper introduced the key technique of driving PC/104 data acquiring card on Lab VIEW platform by using DLL, given a concrete method of using ADT700. Test result shows the system can work effectively and stably, satisfied the design desire.

The technology of data mining is more important in recent years, and it is generally applied to commercial forecast and decision supports. Sequential pattern mining algorithms in the field of data mining play one of the important roles. Many of sequential pattern mining algorithms were proposed to improve the efficiency of data mining or save the utility rate of memory. So, our major study tries to improve the efficiency of sequential pattern mining algorithms. We propose a new algorithm - GRS (A Gradational Reduction Approach for Mining Sequential Patterns) which is an efficient algorithm of mining sequential patterns. GRS algorithm uses gradational reduction mechanism to reduce the length of transactions and uses GraDec function to avoid generating large number of infrequent sequential patterns; and it is very suitable to mine the transactions of databases whose record lengths are very long. The GRS algorithm only generates some sequences which are very possible to be frequent. So, the GRS algorithm can decrease a large number of infrequent sequences and increase the utility rate of memory.

Utility mining has recently been an emerging topic in the field of data mining. It finds out high-utility itemsets by considering both the profits and quantities of items in transactions. In real applications, however, utility mining may have a bias if items are not always on shelf. On-shelf utility mining is then proposed, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. In the past, a two-phase on-shelf utility mining was proposed to discover the desired patterns in on-shelf utility mining. It, however, adopted the level-wise mining way to find the patterns. To speed up the execution efficiency, a three-scan mining approach is thus proposed in the paper to efficiently discover high on-shelf utility itemsets. The proposed approach utilizes an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from transactions. At last, the experimental result...