Mining user navigational patterns in dynamically changing environments

Udechukwu, Ajumobi; Barker, Ken; Alhajj, Reda

doi:10.1109/itcc.2004.1286666

Cited by 3 publications

(1 citation statement)

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“…Motifs have been addressed in literature as recurring patterns, frequent trends, approximately repeated sequences, shapes, episodes, or frequent subsequences; all having the same goal. Regardless of these namings, two classes of definitions for motifs exist in the literature, namely top K ‐frequent motifs for K ∈ ℕ and nearest‐neighbor motif .…”

Section: Time Series Motif Definitionsmentioning

confidence: 99%

Survey on time series motif discovery

Torkamani

Lohweg

2017

WIREs Data Min & Knowl

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Last decades witness a huge growth in medical applications, genetic analysis, and in performance of manufacturing technologies and automatised production systems. A challenging task is to identify and diagnose the behavior of such systems, which aim to produce a product with desired quality. In order to control the state of the systems, various information is gathered from different types of sensors (optical, acoustic, chemical, electric, and thermal). Time series data are a set of real-valued variables obtained chronologically. Data mining and machine learning help derive meaningful knowledge from time series. Such tasks include clustering, classification, anomaly detection and motif discovery. Motif discovery attempts to find meaningful, new, and unknown knowledge from data. Detection of motifs in a time series is beneficial for, e.g., discovery of rules or specific events in a signal. Motifs provide useful information for the user in order to model or analyze the data. Motif discovery is applied to various areas as telecommunication, medicine, web, motion-capture, and sensor networks. This contribution provides a review of the existing publications in time series motif discovery along with advantages and disadvantages of existing approaches. Moreover, the research issues and missing points in this field are highlighted. The main objective of this focus article is to serve as a glossary for researchers in this field.

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Section: Time Series Motif Definitionsmentioning

confidence: 99%