Matching and retrieving sequential patterns using regression
Titel:
Matching and retrieving sequential patterns using regression
Auteur:
Hansheng Lei Venu Govindaraju
Verschenen in:
Web intelligence and agent systems
Paginering:
Jaargang 3 (2005) nr. 4 pagina's 261-270
Jaar:
2005-12-22
Inhoud:
Sequential pattern mining can prove to be very useful for predicating future activities, interpreting recurring phenomena, extracting similarities in a series of events, etc. For example, in the NASDAQ market, the problem of finding stocks whose closing prices are always about β_0 higher than or β_1 times the stocks of a given company, reduces to linear pattern retrieval: given query X, find all sequences Y from the database S so that, Y=β_0+β_1X with confidence C. In this paper, we introduce a novel approach using the Simple Linear Regression (SLR) model to match and retrieve sequential patterns. We extend the one-dimensional R^2 model to ER^2 for multi-dimensional sequence matching. In addition, we present the SLR + FFT pruning technique to speed up data retrieval without incurring any false dismissal. Experimental results on both synthetic and real datasets show that the pruning ratio of SLR + FFT can be above 99%. Applying the retrieval technique to real stocks resulted in the discovery many interesting patterns, some of which are presented in the paper. Also, using ER^2 as the similarity measure for on-line signature recognition yielded high accuracy.
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