Data = Normal + Anomalous + Noise

Research output: Contribution to journalArticle

Abstract

Our world at the micro, macro and personal level is now highly instrumented. A consequence of this instrumentation is that now it is possible to obtain fine-grained data about almost anything of interest. Once we focus on an application or a domain, it is reasonable to assume that much of the data obtained captures the "normal" behavior of the underlying phenomenon. Historically, "knowledge discovery," if any, has been triggered by the non-normal or anomalous part of the data. In this talk I will present some classic examples of data anomalies and how their discovery has changed our understanding of the world. Then I will present a modern and algorithmic viewpoint of anomaly detection as is currently practiced in the data mining community.

Original languageEnglish
Pages (from-to)3
Number of pages1
JournalUnknown Journal
Volume134
Publication statusPublished - 2012
Externally publishedYes

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Data mining
Macros
Data acquisition
anomaly
data mining
instrumentation
world

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Software

Cite this

Data = Normal + Anomalous + Noise. / Chawla, Sanjay.

In: Unknown Journal, Vol. 134, 2012, p. 3.

Research output: Contribution to journalArticle

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