Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets

U
UDM
2005
International Workshop on Ubiquitous Data Management
Abstract - Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets - First Findings

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International Workshop on Ubiquitous Data Management

Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets - First Findings

Tokyo, Japan

April 04-April 04

ISBN: 0-7695-2411-7

	ASCII Text	x
	Rajanish Dass, Ambuj Mahanti, "Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets - First Findings," Ubiquitous Data Management, International Workshop on, pp. 113-120, International Workshop on Ubiquitous Data Management, 2005.

	BibTex	x
	@article{ 10.1109/UDM.2005.10, author = {Rajanish Dass and Ambuj Mahanti}, title = {Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets - First Findings}, journal ={Ubiquitous Data Management, International Workshop on}, volume = {0}, year = {2005}, isbn = {0-7695-2411-7}, pages = {113-120}, doi = {http://doi.ieeecomputersociety.org/10.1109/UDM.2005.10}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Ubiquitous Data Management, International Workshop on TI - Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets - First Findings SN - 0-7695-2411-7 SP113 EP120 A1 - Rajanish Dass, A1 - Ambuj Mahanti, PY - 2005 KW - null VL - 0 JA - Ubiquitous Data Management, International Workshop on ER -

Rajanish Dass, Indian Institute of Management Calcutta

Ambuj Mahanti, Indian Institute of Management Calcutta

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/UDM.2005.10

Finding frequent patterns from databases has been the most researched topic in association-rule mining. Business-Intelligence using data-mining has felt an increased thrust for real-time frequent pattern mining algorithms finding huge demand from numerous realtime business applications like e-commerce, recommender-systems, group-decision-supportsystems, supply-chain-management, to name a few. Last decade has seen development of mind-whelming algorithms, among which, vertical-mining algorithms have been found to be very effective. However, with dense-datasets, the performances of these algorithms significantly degrade. Moreover, these algorithms are not suited to respond to the real-time need. In this paper, we describe BDFS(b)-diff-sets, an algorithm to perform real-time frequent pattern mining using diffsets and using an intelligent staged search technique, by-passing usual breadth-first and depth-first searchtechniques. Empirical evaluations show that our algorithm can make a fair estimation of the probable frequent-patterns reacting to the user-defined time bound and reaches some of the longest frequent patterns much faster than the existing algorithms.

Citation:

Rajanish Dass, Ambuj Mahanti, "Implementing BDFS(b) with Diff-Sets for Real-Time Frequent Pattern Mining in Dense Datasets - First Findings," udm, pp.113-120, International Workshop on Ubiquitous Data Management, 2005

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