文章基本信息

标题：Pattern Discovery for Multiple Data Sources Based on Item Rank
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作者：Arti Deshpande ; Anjali Mahajan ; A Thomas 等
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2017
卷号：7
期号：1
页码：37
DOI：10.5121/ijdkp.2017.7104
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Retail company’s data may be geographically spread in different locations due to huge amount of data andrapid growth in transactions. But for decision making, knowledge workers need integrated data of all sites.Therefore the main challenge is to get generalized patterns or knowledge from the transactional datawhich is spread at various locations. Transporting data from those locations to server site increases thecost of transportation of data and at the same time finding patterns from huge data on the server increasesthe time and space complexity. Thus multi-database mining plays a vital role to extract knowledge fromdifferent data sources. Thus the technique proposed finds the patterns on various sites and instead oftransporting the data, only the patterns from various locations get transported to the server to find finaldeliverable pattern. The technique uses the ranking algorithm to rank the items based on their profit, dateof expiry and stock available at each location. Then association rule mining (ARM) is used to extractpatterns based on ranking of items. Finally all the patterns discovered from various locations are mergedusing pattern merger algorithm. Proposed algorithm is implemented and experimental results are takenfor both classical association rule mining on integrated data and for datasets at various sources. Finallyall patterns are combined to discover actionable patterns using pattern merger algorithm given in sectionV.
关键词：Association Rule Mining; Pattern Discovery; Data Mining; Ranking