Mining single pass weighted pattern tree

Olivia Castelino, Preetham Kumar, Srivatsa Maddodi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Weighted tree mining has become an important research topic in Data mining. There are several algorithms for mining Frequent Pattern trees. FP growth algorithm using FP tree has been considered for frequent pattern mining because of its enormous performance and development compared to the candidate generation model of Apriori. The purpose of our work is to provide a tree structure for incremental and interactive weighted pattern mining by only one database scan. It is applied to existing Compact pattern (CP) tree. CP tree dynamically achieves frequency-descending prefix tree structure with a single-pass by applying tree restructuring technique and considerably reducing the mining time. It is competent of using prior tree structures and acquires mining outcomes to decrease the computation by incredible amount. Performance analysis show that our tree structure is very efficient for incremental and interactive weighted pattern mining.

Original languageEnglish
Title of host publicationData Engineering and Management - Second International Conference, ICDEM 2010, Revised Selected Papers
Pages117-124
Number of pages8
DOIs
Publication statusPublished - 15-03-2012
Event2nd International Conference on Data Engineering and Management, ICDEM 2010 - Tiruchirappalli, India
Duration: 29-07-201031-07-2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Data Engineering and Management, ICDEM 2010
CountryIndia
CityTiruchirappalli
Period29-07-1031-07-10

Fingerprint

Mining
Tree Structure
Trees (mathematics)
Frequent Pattern Mining
Data mining
Prefix
Performance Analysis
Data Mining
Decrease

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Castelino, O., Kumar, P., & Maddodi, S. (2012). Mining single pass weighted pattern tree. In Data Engineering and Management - Second International Conference, ICDEM 2010, Revised Selected Papers (pp. 117-124). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6411 LNCS). https://doi.org/10.1007/978-3-642-27872-3_18
Castelino, Olivia ; Kumar, Preetham ; Maddodi, Srivatsa. / Mining single pass weighted pattern tree. Data Engineering and Management - Second International Conference, ICDEM 2010, Revised Selected Papers. 2012. pp. 117-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Castelino, O, Kumar, P & Maddodi, S 2012, Mining single pass weighted pattern tree. in Data Engineering and Management - Second International Conference, ICDEM 2010, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6411 LNCS, pp. 117-124, 2nd International Conference on Data Engineering and Management, ICDEM 2010, Tiruchirappalli, India, 29-07-10. https://doi.org/10.1007/978-3-642-27872-3_18

Mining single pass weighted pattern tree. / Castelino, Olivia; Kumar, Preetham; Maddodi, Srivatsa.

Data Engineering and Management - Second International Conference, ICDEM 2010, Revised Selected Papers. 2012. p. 117-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6411 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Castelino O, Kumar P, Maddodi S. Mining single pass weighted pattern tree. In Data Engineering and Management - Second International Conference, ICDEM 2010, Revised Selected Papers. 2012. p. 117-124. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-27872-3_18