Frequent pattern mining: current status and future directions Export

@article{citeulike:1608289, abstract = {Abstract\ \ Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.}, author = {Han, Jiawei and Cheng, Hong and Xin, Dong and Yan, Xifeng}, citeulike-article-id = {1608289}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10618-006-0059-1}, citeulike-linkout-1 = {http://www.ingentaconnect.com/content/klu/dami/2007/00000015/00000001/00000059}, citeulike-linkout-2 = {http://www.springerlink.com/content/9p5633hm18x55867}, day = {7}, doi = {10.1007/s10618-006-0059-1}, issn = {1384-5810}, journal = {Data Mining and Knowledge Discovery}, keywords = {mining, pattern}, month = {August}, number = {1}, pages = {55--86}, posted-at = {2009-10-26 22:39:47}, priority = {2}, publisher = {Springer}, title = {Frequent pattern mining: current status and future directions}, url = {http://dx.doi.org/10.1007/s10618-006-0059-1}, volume = {15}, year = {2007} }

TY - JOUR ID - citeulike:1608289 L3 - citeulike-article-id:1608289 N2 - Abstract  Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications. IS - 1 JF - Data Mining and Knowledge Discovery SN - 1384-5810 EP - 86 TI - Frequent pattern mining: current status and future directions PB - Springer VL - 15 SP - 55 KW - mining KW - pattern AU - Han, Jiawei AU - Cheng, Hong AU - Xin, Dong AU - Yan, Xifeng PY - 2007/08/07/ UR - http://dx.doi.org/10.1007/s10618-006-0059-1 ER -