R-Diffset vs. IR-Diffset: Comparison Analysis in Dense and Sparse Data
Julaily Aida Jusoh,Sharifah Zulaikha Tengku Hassan,Wan Aezwani Wan Abu Bakar,Syarilla Iryani A. Saany,M.K. Awang,Norlina Udin @ Kamaruddin +5 more
TL;DR: In this article , the authors compared the performance of the R-Diffset and IR-diffset algorithms in the mining of sparse and dense data in the vertical data format, where the important patterns encountered are categorized as recurrent/frequent and non-recurrent/infrequent.
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Abstract: The mining of concealed information from databases using Association Rule Mining seems to be promising. The successful extraction of this information will give a hand to many areas by aiding them in the process of finding solutions, economic projecting, commercialization policies, medical inspections, and numbers of other problems. ARM is the most outstanding method in the mining of remarkable related configurations from any groups of information. The important patterns encountered are categorized as recurrent/frequent and non-recurrent/infrequent. Most of the previous data mining methods concentrated on horizontal data set-ups. Nevertheless, recent studies have shown that vertical data formats are becoming the main concerns. One example of vertical data format is Rare Equivalence Class Transformation (R-Eclat). Due to its efficacy, R-Eclat algorithms have been commonly applied for the processing of large datasets. The R-Eclat algorithm is actually comprised of four types of variants. However, our work will only focus on the R-Diffset variant and Incremental RDiffset (IR-Diffset). The performance analysis of the R-Diffset and IR-Diffset algorithms in the mining of sparse and dense data are compared. The processing time for R-Diffset algorithm, especially for sequential processing is very long. Thus, the incremental R-Diffset (IR-Diffset) has been established to solve this problem. While R-Diffset may only process the nonrecurrent itemsets mining process in sequential form, IR-Diffset on the other hand has the capability to execute sequential data that have been fractionated. The advantages of this newly developed IR-Diffset may become a potential candidate in providing a time-efficient data mining process, especially those involving the large sets of data. Keywords—R-Diffset; IR-Diffset; dense data; sparse data; comparison analysis
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References
Mining association rules between sets of items in large databases
Rakesh Agrawal,Tomasz Imielinski,Arun N. Swami +2 more
- 01 Jun 1993
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
•Proceedings Article
Fast algorithms for mining association rules
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 01 Jul 1998
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
- 16 May 2000
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
Introduction to Data Mining
Daniel T. Larose
- 27 Jan 2005
TL;DR: This book discusses data mining through the lens of cluster analysis, which examines the relationships between data, clusters, and algorithms, as well as some of the techniques used to solve this problem.
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