Book ChapterDOI
DHPTID-HYBRID algorithm: a hybrid algorithm for association rule mining
Shilpa Sonawani,Amrita Mishra +1 more
- pp 149-160
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TLDR
A hybrid approach for association rule mining is described, based on coupling of best part of two well known ARM algorithms, AprioriTid and DHP with a binary approach which significantly improves the performance.Abstract:
Direct Hashing and Pruning algorithm of ARM performs well at initial passes by smaller candidate 2-itemset generation and turns out to be very powerful to facilitate initial itemset generation. Efficient pruning technique of AprioriTid algorithm is highly effective for frequent itemset generation in the later passes. Theoretical and practical studies reveals the underneath facts for the scope of improvement for both the algorithms. This paper describes a hybrid approach for association rule mining, based on coupling of best part of two well known ARM algorithms, AprioriTid and DHP with a binary approach which significantly improves the performance.read more
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Proceedings ArticleDOI
A study on time based association rule mining on spatial-temporal data for intelligent transportation applications
Swathi Lanka,Sanjay Kumar Jena +1 more
TL;DR: This paper presents an analysis on different data mining algorithms, soft and evolution computation techniques which are focused on extracting transactional and time based association rules.
Dissertation
Collaborative Computing Cloud: Architecture and Management Platform
TL;DR: A platform for mobile cloud computing is proposed that integrates a dynamic real-time resource scheduling, tracking, and forecasting system; an autonomous resource management system; and a cloud management capabi lity for cloud services that hides the heterogeneity, dynamicity, and geographical diversi ty concerns from the cloud operation.
References
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Proceedings Article
Fast algorithms for mining association rules
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.
Proceedings ArticleDOI
An effective hash-based algorithm for mining association rules
TL;DR: The number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck, and allows us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly.
Journal ArticleDOI
A minimal perfect hashing scheme to mining association rules from frequently updated data
TL;DR: An algorithm for mining association rules based on a minimal perfect hashing scheme is presented, which is especially suitable for handling very large databases containing huge amounts of transactions and frequently updated data.