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Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations

Electronic Theses of Indian Institute of Science

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Title Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations
 
Creator Laxman, Srivatsan
 
Subject Data Mining
Databases - Data Mining
Hidden Markov Models
Event (Electrical Engineering) - Frequency
Temporal Data Mining
Episodes (Temporal Patterns)
Event Sequences
Frequent Episodes
Episode Discovery
Fast Algorithms
Pattern Discovery
Computer Science
 
Description Temporal data mining is concerned with the exploration of large sequential (or temporally ordered) data sets to discover some nontrivial information that was previously unknown to the data owner. Sequential data sets come up naturally in a wide range of application domains, ranging from bioinformatics to manufacturing processes. Pattern discovery refers to a broad class of data mining techniques in which the objective is to unearth hidden patterns or unexpected trends in the data. In general, pattern discovery is about finding all patterns of 'interest' in the data and one popular measure of interestingness for a pattern is its frequency in the data. The problem of frequent pattern discovery is to find all patterns in the data whose frequency exceeds some user-defined threshold. Discovery of temporal patterns that occur frequently in sequential data has received a lot of attention in recent times. Different approaches consider different classes of temporal patterns and propose different algorithms for their efficient discovery from the data. This thesis is concerned with a specific class of temporal patterns called episodes and their discovery in large sequential data sets.

In the framework of frequent episode discovery, data (referred to as an event sequence or an event stream) is available as a single long sequence of events. The ith event in the sequence is an ordered pair, (Et,tt), where Et takes values from a finite alphabet (of event types), and U is the time of occurrence of the event. The events in the sequence are ordered according to these times of occurrence. An episode (which is the temporal pattern considered in this framework) is a (typically) short partially ordered sequence of event types. Formally, an episode is a triple, (V,
 
Contributor Sastry, P S
 
Date 2008-10-13T09:41:50Z
2008-10-13T09:41:50Z
2008-10-13T09:41:50Z
2006-03
 
Type Thesis
 
Identifier http://hdl.handle.net/2005/375
 
Language en_US
 
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