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PATTERN SPACE MAINTENANCE FOR DATA UPDATES AND INTERACTIVE MINING *
Author(s) -
Feng Mengling,
Dong Guozhu,
Li Jinyan,
Tan YapPeng,
Wong Limsoon
Publication year - 2010
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2010.00360.x
Subject(s) - computer science , tree (set theory) , space (punctuation) , software maintainer , generator (circuit theory) , data mining , trie , pattern recognition (psychology) , data structure , artificial intelligence , machine learning , mathematics , mathematical analysis , power (physics) , physics , quantum mechanics , programming language , operating system
This article addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in‐depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator‐Enumeration Tree ( GE‐tree ), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE‐tree , we propose two novel algorithms, Pattern Space Maintainer + ( PSM+ ) and Pattern Space Maintainer − ( PSM− ), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state‐of‐the‐art methods by an order of magnitude.