Efficient mining of frequent items coupled with weight and /or support over progressive databases

Authors:
B. N. Keshavamurthy;Mitesh Sharma;Durga Toshniwal
Affiliations:
Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India;Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India;Department of Electronics and Computer Engineering, Indian Institute of Technology, Roorkee, Uttarakhand, India
Venue:
ICDEM'10 Proceedings of the Second international conference on Data Engineering and Management
Year:
2010

Citing 9
Cited 1

Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Frequent Items in Data Streams

ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Finding recent frequent itemsets adaptively over online data streams

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

Knowledge and Information Systems
Efficient mining of weighted interesting patterns with a strong weight and/or support affinity

Information Sciences: an International Journal
Approximate frequency counts over data streams

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A General Model for Sequential Pattern Mining with a Progressive Database

IEEE Transactions on Knowledge and Data Engineering
Mining frequent itemsets in data streams using the weighted sliding window model

Expert Systems with Applications: An International Journal
Efficient Mining of Weighted Frequent Patterns over Data Streams

HPCC '09 Proceedings of the 2009 11th IEEE International Conference on High Performance Computing and Communications

CSSF-trie structure to mine constraint sequential patterns from progressive database

International Journal of Knowledge Engineering and Data Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

In recent times, mining of frequent pattern in progressive databases is a very attractive area of research. In real world applications such as market basket analysis of retail-shop where the items are associated static attribute weight, which reflects each item has different importance and dynamic attribute support, which represents the frequency of an item. The mining of items which is having both static and dynamic attributes reveals an important knowledge than the traditional patterns. We use two notions in the process of mapping input items to general tree structure. One, the product of dynamic attribute value support and static attribute weight should be greater than user defined threshold. Second, the dynamic attribute value support should be greater than user defined threshold. Our proposed approach uses sliding window and apriori's antimonotone principle in mining the items associated weight and/or support over progressive databases.