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Modern algorithms for mining frequent itemsets face the noteworthy deterioration of performance when minimum support tends to decrease, especially for sparse datasets. Long-tailed itemsets, frequent itemsets found at lower minimum support, are significant for present-day applications such as recommender systems. In this study, a novel power set based method named as HARnessing the Power of Power sets (HARPP) for mining frequent itemsets is developed. HARPP is based on the concept of power set from set theory and incorporates efficient data structures for mining. Without storing it entirely in memory, HARPP scans the dataset only once and mines frequent itemsets on the fly. In contrast to state-of-the-art, the efficiency of HARPP increases with a decrease in minimum support that makes it a viable technique for mining long-tailed itemsets. A performance study shows that HARPP is efficient and scalable. It is faster up to two orders of magnitude than FP-Growth algorithm at lower minimum support, particularly when datasets are sparse. HARPP memory consumption is less than that of state-of-the-art by an order of magnitude, on most datasets. [ABSTRACT FROM AUTHOR]

In order to recommend an efficient drawing inspecting expert combination, an expert combination is selected by an expert recommendation algorithm based on Pearson's correlation coefficient and FP-growth. By introducing the Pearson correlation coefficient and the FP-growth association rule algorithm, the expert recommendation algorithm can accurately select the participating experts in the historical project similar to the scale of the project to be reviewed, and combine the experts to calculate and obtain the expert group with the highest fit, namely, the expert combination of project to be reviewed. This expert recommendation algorithm based on Pearson correlation coefficient and FP-growth can effectively recommend a kind of expert group with the highest efficiency of collaborative review, which solves the problem of how to recommend efficient expert combination accurately for drawing inspecting system. [ABSTRACT FROM AUTHOR]

According to the complexity of ocean data, this paper adopts a parallel mining algorithm of association rules to explore the correlation and regularity of oxygen, temperature, phosphate, nitrate and silicate in the ocean. After the marine data is interpolated, this paper utilizes the parallel FP-growth algorithm to mine the data and then briefly analyzes the mining results of the frequent itemsets and association rules. The relationship between the parallel efficiency and the core number of CPU is analyzed through datasets with different scales. The experimental results indicate that the acceleration effect is ideal when each thread scored 200,000-300,000 data, which leads to more than 1.2 times of performance improvement. [ABSTRACT FROM AUTHOR]

La detección de alteraciones de la salud de los animales mediante pruebas de laboratorio es un proceso complejo que implica relacionar diversas variables y factores clínicos de los individuos; por ello, en esta investigación se evaluaron técnicas de inteligencia computacional que contribuyeron a la identificación de patrones de comportamiento de las alteraciones detectadas en las pruebas de hemograma aplicadas en bovinos. Aunque diversos algoritmos de inteligencia computacional son utilizados en la solución de problemas médicos, no se encontró registro de investigaciones en procesos médicos veterinarios. Una vez hecha una minuciosa caracterización de las variables y la evaluación de las técnicas de inteligencia computacional, se determinó que el algoritmo que mejor se ajusta al propósito de análisis de datos planteado es FP-Growth.

In this research we extract knowledge from human resources data, accumulated in IT companies for the right selection of teams to work on software projects. We are looking for interesting and unknown dependencies and connections in the data, based on which managers can form more cohesive and professionally working project teams. The proposed approach to improve the selection of teams working on IT projects is based on association rule mining and can be used by IT managers to select the members of the teams. The approbation of the proposed approach is made using the software product RapidMiner. [ABSTRACT FROM AUTHOR]

Abstract In Electric Management Information System (MIS), there are some users who do not comply with all operation/behavior regulations and make the similar mistakes continuously even though they are not on purpose. These behaviors are a huge threat to the system security. In this paper, we propose a method to detect these regular behaviors with association rules mining algorithm FP-Growth. First, the user log is separated into operation sets each of which contains user operation in a continuous period. Then we divide the operation sets of all users into two catalogs: normal and abnormal based on if a security problem has happened around the corresponding period of operation set. Next, we apply the FP-Growth algorithm in both normal and abnormal operation sets to generate the frequent patterns. Finally, the abnormal pattern is compared with normal ones to determine the regular behaviors that may be dangerous to the system. We test the proposed algorithm in the user log files generated from a simulated electric management information system. The experiment results indicate the proposed method can effectively detect the regular user behavior that could cause the system security problems. [ABSTRACT FROM AUTHOR]

The paper proposes a new method to mine multiple-level fuzzy association rules among items at a concept hierarchy, using fuzzy partition and FP-growth algorithm. The method primarily consists of two phases: one is to find fuzzy grids at each level and the other is to generate multiple-level fuzzy association rules from those frequent patterns. As demonstrate with realistic database and different thresholds, the experimental results illustrate the proposed method are perceived more efficient. [ABSTRACT FROM AUTHOR]

Odoo is an enterprise resource planning information system providing modules to support the basic business function in companies. This research will look into the development of an additional module at Odoo. This module is a data mining module using Market Basket Analysis (MBA) using FP-Growth algorithm in managing OLTP of sales transaction to be useful information for users to improve the analysis of company business strategy. The FP-Growth algorithm used in the application was able to produce multidimensional association rules. The company will know more about their sales and customers' buying habits. Performing sales trend analysis will give a valuable insight into the inner-workings of the business. The testing of the module is using the data from X Supermarket. The final result of this module is generated from a data mining process in the form of association rule. The rule is presented in narrative and graphical form to be understood easier. [ABSTRACT FROM AUTHOR]

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The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality. [ABSTRACT FROM AUTHOR]

The Apriori and FP-Growth are classical algorithms in frequent pattern mining. Since the Apriori has more flaws, the FP-Growth is a more efficient algorithm in stand-alone computing environment. Aiming at the bottlenecks of non-parallel computing in the era of big data, we propose a balanced parallel frequent pattern (BPFT) growth algorithm based on the connect-weight (CW) matrix of items in each transaction, called CWBPFP, which achieves parallel computing based on Spark framework. We use the load balance strategy to group data, and the corresponding code of each frequent item is stored in the relevant group during grouping. The connect information of items in each transaction of each grouped data is stored into a lower triangular connect-weight matrix by each working node. We use the restricted sub-tree to accelerate the speed of producing conditional FP-tree, and employ the connect-weight matrix to avoid the first scanning for the conditional patterns during mining frequent patterns of grouped data. The performance of the parallel mining FP-tree is improved due to the combination of the CW matrix and the restricted sub-tree applied to FP-tree mining process of each node. Experiments show that the CWBPFP has high performance and scalability on big data sets. [ABSTRACT FROM AUTHOR]

Association rule data mining is an important technique for finding important relationships in large datasets. Several frequent itemsets mining techniques have been proposed using a prefix-tree structure, FP-tree, a compressed data structure for database representation. The DIFFset data structure has also been shown to significantly reduce the run time and memory utilization of some data mining algorithms. Experimental results have demonstrated the efficiency of the two data structures in frequent itemsets mining. This work proposes FDM, a new algorithm based on FP-tree and DIFFset data structures for efficiently discovering frequent patterns in data. FDM can adapt its characteristics to efficiently mine long and short patterns from both dense and sparse datasets. Several optimization techniques are also outlined to increase the efficiency of FDM. An evaluation of FDM against three frequent itemset data mining algorithms, dEclat, FP-growth, and FDM* (FDM without optimization), was performed using datasets having both long and short frequent patterns. The experimental results show significant improvement in performance compared to the FP-growth, dEclat, and FDM* algorithms. [ABSTRACT FROM AUTHOR]

With the booming of social media and health informatics, there exists a pressing need for a powerful tool to sustain comprehensive analysis of public and personal health information. In particular, it should be able (1) to maximize the discovery of association rules amongst data items and (2) to handle the rapid growing data scale. The FP-Growth algorithm is a salient association rule learning method in exploring potential relation in database possibly with a lack of priori knowledge. It has the merits of low time & space complexity, whereas it cannot handle negative association rules which is necessary in comprehensive mining of health data. In order to enable comprehensive discovery of association rules, this study extends the FP-Growth algorithm to mine both positive and negative frequent patterns, namely the PNFP-Growth framework. The extended approach also adopts a prune strategy to filter out misleading patterns to the most by correlating the negative data items and the positive ones. Experiments had been performed to evaluate the performance of the PNFP-Growth over a public data set and a database consisting of thousands of people's real health examination information (collected within 5 years from the date of this publication). The results indicate that (1) the PNFP-Growth can excavate more patterns than the traditional counterpart does while it is still highly efficient, and (2) the analysis upon the health examination data is informative and well complies with the clinical practices, e.g., more than 30 % people suffering from hypertension are having high systolic pressure and liver problems. [ABSTRACT FROM AUTHOR]

FP-Growth algorithm is an algorithm of association rules that does not generate a set of candidate, so it has very high practical value in face of the rapid growth of data volume in wisdom medical treatment. Because FP-Growth is a memory-resident algorithm, it will appear to be powerless when it is used for massive data sets. The paper combines Hadoop and FP-Growth algorithm and through the actual analysis of traditional Chinese medicine (TCM) data compares the performance in two different environments of stand-alone and distributed. The experimental results show that FP-Growth algorithm has a great advantage in the processing and execution of massive data after the MapReduce parallel model, so that it will have better development prospects for intelligent medical treatment. [ABSTRACT FROM AUTHOR]

Data mining field concentrates on theories, concepts, methodologies and mainly on extraction of useful knowledge from large amounts of data for decision making. During their day to day activities healthcare industry generates large amounts of healthcare information that has not been efficiently used to extract unknown information. Therefore the discovery of interesting and useful information usually remains hidden. Diabetes is a healthcare problem and is increasing at a greater rate with every passing year. If not recognized early, can lead to severe health problems, even in organ failures. Several data mining techniques like clustering, classification, association rule mining are used to identify early symptoms of the diseases and stopping them getting to a chronic level. In this paper, an efficient approach has been designed for prediction of risk of getting diabetes using diabetes database. The approach in this paper used more than one data mining techniques showing enhanced result in disease prediction. The data for diabetes is collected and processed to facilitate the mining process. Firstly, the preprocessed database is mined to extract frequent patterns related to diabetes using FP-Growth algorithm. After that ID3 algorithm approach has been used as the training algorithm to depict the risk of diabetes using a Decision Tree. [ABSTRACT FROM AUTHOR]

This article proposes an innovative utility sentient approach for the mining of interesting association patterns from transaction databases. First, frequent patterns are discovered from the transaction database using the FP-Growth algorithm. From the frequent patterns mined, this approach extracts novel interesting association patterns with emphasis on significance, utility, and the subjective interests of the users. The experimental results portray the efficiency of this approach in mining utility-oriented and interesting association rules. A comparative analysis is also presented to illustrate our approach's effectiveness.

Automation in pharmacies has achieved innovative levels of effectiveness and savings. In the present day, automated pharmacies are facing extremely large demands of prescription orders specifically at the central fill pharmacies that distribute drugs to retail pharmacies. As a result, improvements are necessary to the Robotic Prescription Dispensing System (RPDS) and RPDS planogram to increase the throughput of prescriptions. RPDS planogram defines where to allocate the dispensers inside the robotic unit and how to distribute them among the multiple robotic units. This research utilizes the pharmacy prescriptions database to extract useful knowledge to improve different strategies in pharmacy automation by using a data mining approach. In this study, a data mining tool is proposed to enhance pharmacy automation. Frequent Pattern Growth (FP-growth) approach, which is one of the algorithms of Association Rule Mining (ARM), is applied to an actual prescriptions database of a central fill pharmacy to study the associations within the prescribed drug regime. The FP-growth application in a prescriptions database is novel; thus, FP-growth is tested on both sequential mode, and parallel mode by using a distributed platform Hadoop and MapReduce paradigm. Two types of association rules are extracted: 1) associations among different drugs that involve their different dosage strengths and manufacturers; and 2) associations that include only information about different drug generic and brand names. The importance of the extracted association rules is evaluated by the use of different measures, including the support, confidence, lift and conviction. The discovered rules disclose strong associations among the purchased drugs that improve the allocation and distribution of dispensers among the robotic units, in addition to enhancements in other pharmacy managerial strategies. [ABSTRACT FROM AUTHOR]

In this paper, we present a sparse memory allocation data structure for sequential and parallel data mining. We explored three algorithms utilizing the proposed data structure: MASP-tree, apriori-TID, and FP-growth. We modified the data structure of apriori-TID and FP-growth algorithms to reduce memory allocation cost. Five data sets are used for comparison. The results show that the modified apriori-TID has a higher speed-up than the modified FP-growth when the proposed data structure is used. A maximum speed-up of 3.42 is observed when MASP algorithm is tested. [ABSTRACT FROM AUTHOR]

Identifying related offences in a criminal investigation is an important goal for crime analysts. This can deliver evidence that can assist in apprehension of suspects and better attribution of past crimes. The use of pattern based approaches has the potential to assist crime experts in discovering new patterns of criminal activity. Hence, research in this area continues. This paper revisits frequent pattern growth models for crime pattern mining. Frequent pattern (FP) based approaches, such as the FP-Growth model, have been identified to be more effective than techniques proposed in the past, such as Apriori. Therefore, this research proposes a descriptive statistical approach, based on a quartile (floor-ceil) function, for the minimum support threshold (MST) choice selection, which is a major decision step in the pruning phase of the Traditional FP-Growth (TFPG) model. Our revised frequent pattern growth (RFPG) model further proposes a Pattern-pattern ( Pp ) paradigm to identify tuples of subtle crime pattern(s) sequences or recurring trends in criminal activity. We present empirical results in order to guide intended audience about future decisions or research regarding this model. Results indicate that RFPG is more promising than TFPG and will always ensure the utilisation of a reasonable percentage of the crime dataset, in order to produce more reliable and sufficiently informative patterns or trends. [ABSTRACT FROM AUTHOR]

The objective of this study is to apply data mining techniques to determine factors that are commonly associated with liver cancer incidence, using an anonymized data set of 6064 patients from the British Columbia Cancer Agency (BCCA). The association rules indicate that in BC the patient demographic factors associated with increased liver cancer include: age ranges 60-69, male gender, and geographic location in the Greater Vancouver area. The main factors associated with decreased survivability in BC were being male and in the age range 70-79. In the Yukon, being male and in the age range 60-69 was the main factor associated with both increased incidence of liver cancer and decreased survivability. [ABSTRACT FROM AUTHOR]

Association rule mining is an important process in the field of data mining, discovering not trivial relationship between a large set of data items. For extracting association rules from an online retail transaction data set we used two of the most known algorithms i.e. FP-Growth and Apriori. Based on the particularities of processes built, we performed a statistical analysis to illustrate the efficiency, precision and accuracy of data mining techniques used. [ABSTRACT FROM AUTHOR]