A sliding windows based dual support framework for discovering emerging trends from temporal data

Knowledge-Based Systems

Volumn 23, Issue 4, 2010, Pages 316-322

  Khan, M Sulaiman ^a   Coenen, F ^b   Reid, D ^a   Patel, R ^c   Archer, L ^c  

^a LIVERPOOL HOPE UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF LIVERPOOL   (United Kingdom)

^c NONE   (United Kingdom)

Author keywords

Association rule mining; Jumping emerging patterns; Sliding window; Temporal trends; Time series

Indexed keywords

APRIORI; ASSOCIATION RULE MINING; COMPUTATIONAL COSTS; DATA SETS; EMERGING PATTERNS; EMERGING TRENDS; EXPERIMENTAL SECTION; ITEM SETS; ITEMSET; JUMPING EMERGING PATTERN; LARGE DATASETS; NOVEL TECHNIQUES; SEARCH SPACES; SLIDING WINDOW; SLIDING WINDOW TECHNIQUES; TEMPORAL DATA; TEMPORAL TRENDS; TIME-SERIES DATA; TIME-STAMPED DATA; TREND ANALYSIS;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; TIME SERIES;

DECODING;

EID: 77950857411     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2009.11.005     Document Type: Article

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