Community trend outlier detection using soft temporal pattern mining

Manish Gupta, Jing Gao, Yizhou Sun, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Numerous applications, such as bank transactions, road traffic, and news feeds, generate temporal datasets, in which data evolves continuously. To understand the temporal behavior and characteristics of the dataset and its elements, we need effective tools that can capture evolution of the objects. In this paper, we propose a novel and important problem in evolution behavior discovery. Given a series of snapshots of a temporal dataset, each of which consists of evolving communities, our goal is to find objects which evolve in a dramatically different way compared with the other community members. We define such objects as community trend outliers. It is a challenging problem as evolutionary patterns are hidden deeply in noisy evolving datasets and thus it is difficult to distinguish anomalous objects from normal ones. We propose an effective two-step procedure to detect community trend outliers. We first model the normal evolutionary behavior of communities across time using soft patterns discovered from the dataset. In the second step, we propose effective measures to evaluate chances of an object deviating from the normal evolutionary patterns. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting community trend outliers.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Proceedings
Pages692-708
Number of pages17
EditionPART 2
DOIs
StatePublished - 2012
Event2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012 - Bristol, United Kingdom
Duration: Sep 24 2012Sep 28 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7524 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012
CountryUnited Kingdom
CityBristol
Period9/24/129/28/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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