Frequent Itemsets In Large Databases Research Articles

Local support-based partition algorithm for frequent pattern mining

Frequent pattern (itemset) mining is one of the established approaches for knowledge discovery. Minimizing the number of database scans (I/O overhead) is a challenging task in Frequent itemset mining. Partition algorithm is one of the early novel approaches to reduce the database I/O overhead as compared to Apriori algorithm and other related methods. However, Partition algorithm suffers from a significant database I/O overhead (that is, it reads the database twice from the secondary storage) and higher time complexity for computation of frequent itemsets in large databases. In this work, an improved partition algorithm is proposed, which reads the database only once and makes use of local support information to avoid further scans of the database. The proposed algorithm outperforms Apriori and Partition algorithms and shows closer performance to FP-Growth algorithm, in terms of computational time. The proposed method outpaces FP-Growth algorithm in terms of memory usage and is competitive to other algorithms. In terms of database access time, the proposed method exhibits better performance over FP-Growth, Partition and Apriori methods.

Mining frequent itemsets in large databases: The hierarchical partitioning approach

Although many methods have been proposed to enhance the efficiencies of data mining, little research has been devoted to the issue of scalability – that is, the problem of mining frequent itemsets when the size of the database is very large. This study proposes a methodology, hierarchical partitioning, for mining frequent itemsets in large databases, based on a novel data structure called the Frequent Pattern List (FPL). One of the major features of the FPL is its ability to partition the database, and thus transform the database into a set of sub-databases of manageable sizes. As a result, a divide-and-conquer approach can be developed to perform the desired data-mining tasks. Experimental results show that hierarchical partitioning is capable of mining frequent itemsets and frequent closed itemsets in very large databases.

On pushing weight constraints deeply into frequent itemset mining

There have been many studies on mining frequent itemset (or pattern) in the data mining field because of its broad applications in mining association rules, correlations, graph patterns, constraint based frequent patterns, sequential patterns, and many other data mining tasks. One of major challenges in frequent pattern mining is a huge number of result patterns. As the minimum threshold becomes lower, an exponentially large number of itemsets are generated. Therefore, pruning unimportant patterns effectively in mining process is one of main topics in frequent pattern mining. In weighted frequent pattern mining, not only support but also weight are used and important patterns can be detected. In this paper, we propose two efficient algorithms for mining weighted frequent itemsets in which the main approaches are to push weight constraints into the Apriori algorithm and the pattern growth algorithm respectively. Additionally, we show how to maintain the downward closure property in mining weighted frequent itemsets. In our approach, the normalized weights within the weight range are used according to the importance of items. A weight range is used to restrict weights of items and a minimum weight is utilized to balance between weight and support of items for pruning the search space. Our approach generates fewer but important weighted frequent itemsets in large databases, particularly dense databases with low minimum supports. An extensive performance study shows that our algorithm outperforms previous mining algorithms. In addition, it is efficient and scalable.

FIT: A Fast Algorithm for Discovering Frequent Itemsets in Large Databases

Association rules mining is an important data mining problem that has been studied extensively. In this paper, a simple but Fast algorithm for Intersecting attributes lists using hash Tables (FIT) is presented. FIT is designed for efficiently computing all the frequent itemsets in large databases. It deploys an idea similar to Eclat but has a much better computational performance than Eclat due to two reasons: 1) FIT makes fewer total number of comparisons for each intersection operation between two attributes lists, and 2) FIT significantly reduces the total number of intersection operations. Our experimental results demonstrate that the performance of FIT is much better than that of Eclat and Apriori algorithms.