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As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study uses the Generalized Sequential Pattern (GSP) algorithm, which can be used to determine the behavior patterns of users in each transaction and can show relationships or associations between books, both requested simultaneously and sequentially. From the calculations that have been done, 295 frequent sequences are consisting of 3 sequence patterns that are formed from the minimum support of 0.53% or the minimum number of books borrowed, namely 2 books. Three book items have very strong linkages in book lending transactions, namely book code 6690, 2026, and 8131.

In this paper, we review some commonly used data structures and algorithms. We then review two important problems: the creation of the vector-space model that is widely used in the design of information retrieval systems, and the mining of frequent itemsets using the apriori algorithm. We consider two variations of the apriori algorithm: the first is the classical algorithm which computes candidate k-itemsets by first joining frequent (k-1)-itemsets to themselves, and applying the apriori property to prune the generated candidate k-itemsets; the second avoids the join stage in the classical algorithm, and instead, generates candidate k-itemsets directly from rows of the transactions database, followed by application of the apriori property to prune each itemset so determined. Finally, we illustrate appropriate data structures and algorithms that when put together, provide efficient implementations of our solution to the problems mentioned.Keywords: data structures, algorithms, vector-space model, frequent itemsets mining, apriori algorithm

Apriori algorithm, as a typical frequent itemsets mining method, can help researchers and practitioners discover implicit associations from large amounts of data. In this work, a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets, is proposed, which is based on an evolution-communication tissue-like P system with promoters and inhibitors. The structure of the ECTPPI-Apriori algorithm is tissue-like and the evolution rules of the algorithm are object rewriting rules. The time complexity of ECTPPI-Apriori is substantially improved from that of the conventional Apriori algorithms. The results give some hints to improve conventional algorithms by using membrane computing models.

Mining association rules with multiple minimum supports is an important generalization of the association-rule-mining problem, which was recently proposed by Liu et al. Instead of setting a single minimum support threshold for all items, they allow users to specify multiple minimum supports to reflect the natures of the items, and an Apriori-based algorithm, named MSapriori, is developed to mine all frequent itemsets. In this paper, we study the same problem but with two additional improvements. First, we propose a FP-tree-like structure, MIS-tree, to store the crucial information about frequent patterns. Accordingly, an efficient MIS-tree-based algorithm, called the CFP-growth algorithm, is developed for mining all frequent itemsets. Second, since each item can have its own minimum support, it is very difficult for users to set the appropriate thresholds for all items at a time. In practice, users need to tune items' supports and run the mining algorithm repeatedly until a satisfactory end is reached. To speed up this time-consuming tuning process, an efficient algorithm which can maintain the MIS-tree structure without rescanning database is proposed. Experiments on both synthetic and real-life datasets show that our algorithms are much more efficient and scalable than the previous algorithm.

Mining frequent patterns in large transactional databases is a highly researched area in the field of data mining. Existing frequent pattern discovering algorithms suffer from many problems regarding the high memory dependency when mining large amount of data, computational and I/O cost. Additionally, the recursive mining process to mine these structures is also too voracious in memory resources. In this paper, we describe a more efficient algorithm for mining complete frequent itemsets from transactional databases. The suggested algorithm is partially based on FP-tree hypothesis and extracts the frequent itemsets directly from the tree. Its memory requirement, which is independent from the number of processed transactions, is another benefit of the new method. We present performance comparisons for our algorithm against the Apriori algorithm and FP-growth.

International audience; In this paper we consider the problem of discovering sequential patterns by handling time constraints as defined in the Gsp algorithm. While sequential patterns could be seen as temporal relationships between facts embedded in the database where considered facts are merely characteristics of individuals or observations of individual behavior, generalized sequential patterns aim to provide the end user with a more flexible handling of the transactions embedded in the database. We thus propose a new efficient algorithm, called Gtc (Graph for Time Constraints) for mining such patterns in very large databases. It is based on the idea that handling time constraints in the earlier stage of the data mining process can be highly beneficial. One of the most significant new feature of our approach is that handling of time constraints can be easily taken into account in traditional levelwise approaches since it is carried out prior to and separately from the counting step of a data sequence. Our test shows that the proposed algorithm performs significantly faster than a state-of-the-art sequence mining algorithm.

Technical report TR01-12. Searching for frequent patterns in transactional databases is considered one of the most important data mining problems. Most current association mining algorithms, whether sequential or parallel, adopt an apriori-like algorithm that requires full multiple I/O scans of the data set and expensive computation to generate the potential frequent items. The recent explosive growth in data collection made the current association rule mining algorithms restricted and inadequate to analyze excessively large transaction sets due to the above mentioned limitations. In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) for parallel mining of frequent patterns, based on FP-growth mining, that uses only two full I/O scans of the database, eliminating the need for generating the candidate items, and distributing the work fairly among processors to achieve near optimum load balance. We have devised partitioning strategies at different stages of the mining process to achieve near optimal balancing between processors. This algorithm has been experimented on databases made of hundreds of thousands of dimensions and size larger than 10 Giga bytes using 64 processors SGI origin shared memory machine. We have successfully tested our algorithm on datasets larger than 50 million transactions. This paper presents the results of our algorithm on different data sizes experimented on different numbers of processors, and studies the effect of these variations on the overall performance. | TRID-ID TR01-12

Association rule mining aims to extract the correlation or causal structure existing between a set of frequent items or attributes in a database. These associations are represented by mean of rules. Association rule mining methods provide a robust but non-linear approach to find associations. The search for association rules is an NP-complete problem. The complexities mainly arise in exploiting huge number of database transactions and items. In this article we propose a new algorithm to extract the best rules in a reasonable time of execution but without assuring always the optimal solutions. The new derived algorithm is based on Quantum Swarm Evolutionary approach; it gives better results compared to genetic algorithms.

The association rule mining plays an important part in the data mining. Association rule mining aims to find rules in the transaction database with the minimum support and minimum confidence which are the user given. In order to find all the frequent item sets from the transaction database efficiently and quickly, an improved Apriori algorithm of mining the association rules in this paper is put forward to solve the bottleneck problems of the traditional Apriori algorithm. First the Boolean matrix array is used to replace the transaction database. Then the “AND” operation and random access characteristics of array are used. Next the mining algorithm is carried out on the Hadoop Platform. According to the number of the Data Nodes of the Hadoop, the matrix is divided into several parts. Each part is operated separately on one Data Node. It can improve the efficiency of the algorithm.