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It addresses two core challenges: creating scalable versions of commonly used data mining methods and developing new algorithms for mining very large data. The editorial discusses why scaling is difficult for data mining, compares progress with linear algebra advances, and previews four selected papers on classification, clustering, association rules, and Bayesian network learning.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/data-mining-and-knowledge-discovery-special-issue-on-scaling-data-mining-algorithms-applications-and-systems-to-massive-data-sets-volume-3-no-3-september-1999/326776/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/data-mining-and-knowledge-discovery-special-issue-on-scaling-data-mining-algorithms-applications-and-systems-to-massive-data-sets-volume-3-no-3-september-1999/326776.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main theme of this special issue?","Question",{"text":112,"@type":113},"The issue focuses on scaling data mining algorithms, applications, and systems to massive data sets by applying high performance computing technology.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What two fundamental challenges does the editorial highlight?",{"text":117,"@type":113},"First, develop scalable versions of commonly used data mining algorithms; second, develop new algorithms for mining very large data sets.",{"name":119,"@type":110,"acceptedAnswer":120},"Which classes of data mining algorithms are covered by the four papers?",{"text":121,"@type":113},"The papers cover classification, clustering, association rule discovery, and learning Bayesian networks.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},326776,1790464540,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","+,*+ 3(5)250$1&(  \n'$7$ 0,1,1* Scaling Algorithms, Applications and Systems  \nThis page intentionally left blank.  \n+,*+ 3(5)250$1&(  \n'$7$ 0,1,1* Scaling Algorithms, Applications and Systems  \nHGLWHG E\\  \n\u003CLNH *XR  \n,PSHULDO &ROOHJH􀀏 8QLWHG.LQJGRP  \n5REHUW *URVVPDQ  \n8QLYHUVLW\\ RI,OOLQRLV DW &KLFDJR  \n$ 6SHFLDO ,VVXH RI  \n'$7$ 0,1,1* $1' .12:/('*( ',6&29(5\u003C 9ROXPH 􀀖􀀏 1R􀀑 􀀓􀀖 􀀋􀀔􀀜􀀜􀀜􀀌  \n./8:(5 $&$'(0,& 38%/,6+(56 New York / Boston / Dordrecht / London / Moscow  \neBook ISBN: 0-306-47011-X  \nPrint ISBN: 0-7923-7745-1  \n©2002 Kluwer Academic Publishers  \nNew York, Boston, Dordrecht, London, Moscow  \nAll rights reserved  \nNo part of this eBook may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, or otherwise, without written consent from the Publisher  \nCreated in the United States of America  \nVisit Kluwer Online at: [http://www.kluweronline.com](http://www.kluweronline.com)  \nand Kluwer's eBookstore at: [http://www.ebooks.kluweronline.com](http://www.ebooks.kluweronline.com)  \n'$7$ 0,1,1*  \n$1'  \n.12:/('*(  \n',6&29(5\u003C  \nVolume 3, No. 3, September 1999  \nSpecial issue on Scaling Data Mining Algorithms, Applications, and Systems to Massive Data Sets by Applying High Performance Computing Technology Guest Editors: Yike Guo, Robert Grossman  \nEditorial   Yike Guo and Robert Grossman 1  \n)XOO 3DSHU &RQWULEXWRUV  \nParallel Formulations of Decision-Tree Classification Algorithms ........................  \n .................... $QXUDJ 6ULYDVWDYD􀀏 (XL􀀐+RQJ +DQ􀀏 9LSLQ .XPDU DQG 9LQHHW 6LQJK 3  \nA Fast Parallel Clustering Algorithm for Large Spatial Databases .................  \n .................................................... +LDRZHL;X􀀏-RFKHQ-lJHUDQG+DQV􀀐3HWHU.ULHJHO 29  \nEffect of Data Distribution in Parallel Mining of Associations .......................  \n..................................................................... 'DYLG :􀀑 &KHXQJ DQG \u003CRQJDJDR ;LDR 57  \nParallel Learning of Belief Networks in Large and Difficult Domains .....................  \n ................................................................................................... \u003C􀀑 ;LDQJ DQG 7􀀑 &KX 81  \nData Mining and Knowledge Discovery, 3, 235-236 (1999) 1999 Kluwer Academic Publishers. Manufactured in The Netherlands.  \n(GLWRULDO  \nYIKE GUO [yg@doc.ic.ac.uk](yg@doc.ic.ac.uk)  \n'HSDUWPHQW RI &RPSXWLQJ􀀏 ,PSHULDO &ROOHJH􀀏 8QLYHUVLW\\ RI /RQGRQ 􀀏 8.  \nROBERT GROSSMAN  \n0DJQLI\\􀀏 ,QF􀀑 􀀉 1DWLRQDO &HQWHUIRU 'DWD 0LQLQJ􀀏 8QLYHUVLW\\ RI,OOLQRLV DW &KLFDJR􀀏 86$  \n[grossman@uic.edu](grossman@uic.edu)  \nHis promises were, as he then was, mighty; But his performance, as he is now, nothing.  \n—Shakespeare, King Henry VIII  \nThis special issue of Data Mining and Knowledge Discovery addresses the issue of scaling data mining algorithms, applications and systems to massive data sets by applying high performance computing technology. With the commoditization of high performance computing using clusters of workstations and related technologies, it is becoming more and more common to have the necessary infrastructure for high performance data mining. On the other hand, many of the commonly used data mining algorithms do not scale to large data sets. Two fundamental challenges are: to develop scalable versions of the commonly used data mining algorithms and to develop new algorithms for mining very large data sets. In other words, today it is easy to spin a terabyte of disk, but difficult to analyze and mine a terabyte of data.  \nDeveloping algorithms which scale takes time. As an example, consider the successful scale up and parallelization of linear algebra algorithms during the past two decades. This success was due to several factors, including: a) developing versions of some standard algorithms which exploit the specialized structure of some linear systems, such as blockstructured systems, symmetric systems, or Toeplitz systems; b) developing new algorithms such as the Wierderman and Lancos algorithms for solving sparse systems; an","cbCaikBPnt3ajW4t","https://ap.wps.com/l/cbCaikBPnt3ajW4t","pdf",1211763,111,"English","# Editorial\n## Scope and motivation for scaling data mining\n## Two fundamental challenges\n## State-of-the-art comparison and research outlook\n## Summary of included papers","[{\"question\":\"What is the main theme of this special issue?\",\"answer\":\"The issue focuses on scaling data mining algorithms, applications, and systems to massive data sets by applying high performance computing technology.\"},{\"question\":\"What two fundamental challenges does the editorial highlight?\",\"answer\":\"First, develop scalable versions of commonly used data mining algorithms; second, develop new algorithms for mining very large data sets.\"},{\"question\":\"Which classes of data mining algorithms are covered by the four papers?\",\"answer\":\"The papers cover classification, clustering, association rule discovery, and learning Bayesian networks.\"}]","Data Mining and Knowledge Discovery - Special issue on Scaling Data Mining Algorithms, Applications, and Systems to Massive Data Sets - Volume 3, No. 3 - September 1999 | PDF",1789975319,280]