Loading…

The PDD Framework for Detecting Categories of Peculiar Data

Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, cu...

Full description

Saved in:

Bibliographic Details
Main Authors:	Shrestha, M., Hamilton, H.J., Yiyu Yao, Konkel, K., Liqiang Geng
Format:	Conference Proceeding
Language:	English
Subjects:	Cancer Cleaning Computer science Density measurement Event detection Frequency Intrusion detection Medical diagnosis Object detection
Online Access:	Request full text
Tags:	Add Tag No Tags, Be the first to tag this record!

cited_by
cites
container_end_page	571
container_issue
container_start_page	562
container_title
container_volume
creator	Shrestha, M. Hamilton, H.J. Yiyu Yao Konkel, K. Liqiang Geng
description	Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.
doi_str_mv	10.1109/ICDM.2006.159
format	conference_proceeding
fullrecord	<record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_4053082</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4053082</ieee_id><sourcerecordid>4053082</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-b4d14bf7aee8e3f6f98696c61ff592002415bd62c4db507a5aa099943671645f3</originalsourceid><addsrcrecordid>eNotjrtOwzAUQC0eEqV0ZGLxDyRcv65tMaGkhUpFdChz5STXxdAS5AQh_p5KMJ3l6Ogwdi2gFAL87bKqn0oJgKUw_oRNpLK6cNrhKZt568CiN9KCsGdsIoyBQluHF-xyGN4AFKKCCbvbvBJf1zVf5HCg7z6_89hnXtNI7Zg-drwKI-36nGjgfeRrar_2KRyFMIYrdh7DfqDZP6fsZTHfVI_F6vlhWd2viiSsGYtGd0I30QYiRypi9A49tihiNP64L7UwTYey1V1jwAYTAnjvtUIrUJuopuzmr5uIaPuZ0yHkn60Go8BJ9QuUd0cz</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>The PDD Framework for Detecting Categories of Peculiar Data</title><source>IEEE Xplore All Conference Series</source><creator>Shrestha, M. ; Hamilton, H.J. ; Yiyu Yao ; Konkel, K. ; Liqiang Geng</creator><creatorcontrib>Shrestha, M. ; Hamilton, H.J. ; Yiyu Yao ; Konkel, K. ; Liqiang Geng</creatorcontrib><description>Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.</description><identifier>ISSN: 1550-4786</identifier><identifier>ISBN: 9780769527017</identifier><identifier>ISBN: 0769527019</identifier><identifier>EISSN: 2374-8486</identifier><identifier>DOI: 10.1109/ICDM.2006.159</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cancer ; Cleaning ; Computer science ; Density measurement ; Event detection ; Frequency ; Intrusion detection ; Medical diagnosis ; Object detection</subject><ispartof>Sixth International Conference on Data Mining (ICDM'06), 2006, p.562-571</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4053082$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27901,54529,54906</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4053082$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Shrestha, M.</creatorcontrib><creatorcontrib>Hamilton, H.J.</creatorcontrib><creatorcontrib>Yiyu Yao</creatorcontrib><creatorcontrib>Konkel, K.</creatorcontrib><creatorcontrib>Liqiang Geng</creatorcontrib><title>The PDD Framework for Detecting Categories of Peculiar Data</title><title>Sixth International Conference on Data Mining (ICDM'06)</title><addtitle>ICDM</addtitle><description>Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.</description><subject>Cancer</subject><subject>Cleaning</subject><subject>Computer science</subject><subject>Density measurement</subject><subject>Event detection</subject><subject>Frequency</subject><subject>Intrusion detection</subject><subject>Medical diagnosis</subject><subject>Object detection</subject><issn>1550-4786</issn><issn>2374-8486</issn><isbn>9780769527017</isbn><isbn>0769527019</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjrtOwzAUQC0eEqV0ZGLxDyRcv65tMaGkhUpFdChz5STXxdAS5AQh_p5KMJ3l6Ogwdi2gFAL87bKqn0oJgKUw_oRNpLK6cNrhKZt568CiN9KCsGdsIoyBQluHF-xyGN4AFKKCCbvbvBJf1zVf5HCg7z6_89hnXtNI7Zg-drwKI-36nGjgfeRrar_2KRyFMIYrdh7DfqDZP6fsZTHfVI_F6vlhWd2viiSsGYtGd0I30QYiRypi9A49tihiNP64L7UwTYey1V1jwAYTAnjvtUIrUJuopuzmr5uIaPuZ0yHkn60Go8BJ9QuUd0cz</recordid><startdate>200612</startdate><enddate>200612</enddate><creator>Shrestha, M.</creator><creator>Hamilton, H.J.</creator><creator>Yiyu Yao</creator><creator>Konkel, K.</creator><creator>Liqiang Geng</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200612</creationdate><title>The PDD Framework for Detecting Categories of Peculiar Data</title><author>Shrestha, M. ; Hamilton, H.J. ; Yiyu Yao ; Konkel, K. ; Liqiang Geng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-b4d14bf7aee8e3f6f98696c61ff592002415bd62c4db507a5aa099943671645f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Cancer</topic><topic>Cleaning</topic><topic>Computer science</topic><topic>Density measurement</topic><topic>Event detection</topic><topic>Frequency</topic><topic>Intrusion detection</topic><topic>Medical diagnosis</topic><topic>Object detection</topic><toplevel>online_resources</toplevel><creatorcontrib>Shrestha, M.</creatorcontrib><creatorcontrib>Hamilton, H.J.</creatorcontrib><creatorcontrib>Yiyu Yao</creatorcontrib><creatorcontrib>Konkel, K.</creatorcontrib><creatorcontrib>Liqiang Geng</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Shrestha, M.</au><au>Hamilton, H.J.</au><au>Yiyu Yao</au><au>Konkel, K.</au><au>Liqiang Geng</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>The PDD Framework for Detecting Categories of Peculiar Data</atitle><btitle>Sixth International Conference on Data Mining (ICDM'06)</btitle><stitle>ICDM</stitle><date>2006-12</date><risdate>2006</risdate><spage>562</spage><epage>571</epage><pages>562-571</pages><issn>1550-4786</issn><eissn>2374-8486</eissn><isbn>9780769527017</isbn><isbn>0769527019</isbn><abstract>Peculiar data are objects that are relatively few in number and significantly different from the other objects in a data set. In this paper, we propose the PDD framework for detecting multiple categories of peculiar data. This framework provides an extensible set of perspectives for viewing data, currently including viewing data as a set of records, attributes, frequencies, intervals, sequences, or sequences of changes. By using these six views of the data, multiple categories of peculiar data can be detected to reveal different aspects of the data. For each view, the framework provides an extensible set of peculiarity measures to detect outliers and other kinds of peculiar data. The PDD framework has been implemented for Oracle and Access. Experiments are reported for data sets concerning Regina weather and NHL hockey.</abstract><pub>IEEE</pub><doi>10.1109/ICDM.2006.159</doi><tpages>10</tpages></addata></record>
fulltext	fulltext_linktorsrc
identifier	ISSN: 1550-4786
ispartof	Sixth International Conference on Data Mining (ICDM'06), 2006, p.562-571
issn	1550-4786 2374-8486
language	eng
recordid	cdi_ieee_primary_4053082
source	IEEE Xplore All Conference Series
subjects	Cancer Cleaning Computer science Density measurement Event detection Frequency Intrusion detection Medical diagnosis Object detection
title	The PDD Framework for Detecting Categories of Peculiar Data
url	http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-24T17%3A34%3A09IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=The%20PDD%20Framework%20for%20Detecting%20Categories%20of%20Peculiar%20Data&rft.btitle=Sixth%20International%20Conference%20on%20Data%20Mining%20(ICDM'06)&rft.au=Shrestha,%20M.&rft.date=2006-12&rft.spage=562&rft.epage=571&rft.pages=562-571&rft.issn=1550-4786&rft.eissn=2374-8486&rft.isbn=9780769527017&rft.isbn_list=0769527019&rft_id=info:doi/10.1109/ICDM.2006.159&rft_dat=%3Cieee_CHZPO%3E4053082%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-b4d14bf7aee8e3f6f98696c61ff592002415bd62c4db507a5aa099943671645f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=4053082&rfr_iscdi=true