DATA MINING ON CUSTOMER SEGMENTATION: A REVIEW
DATA MINING ON CUSTOMER SEGMENTATION: A REVIEW
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客户细分的数据挖掘：回顾
DOI：
10.26483/ijarcs.v8i5.3479
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Er Kiranbir Kaur
中科院分区：
文献类型：
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作者：
Er.Rupampreet kaur;Er Kiranbir Kaur
数据挖掘是从海量数据中提取重要信息并加以保存和有效总结的技术。可以从大量数据中提取隐藏信息。本文的目标是研究用于有效分组数据的方法。分组必须以组可以识别组成员的方式来完成，并且组也可以识别到目前为止仍未分组的成员。数据挖掘中客户细分的不同方法有：聚类法和子群发现法。由于聚类技术的局限性和适用范围，进一步完善了数据挖掘的方法论。
Data mining is used to extract important information from the bulk of data to save it and summarize it in effective manner. The hidden information can be extracted from the large set of data. The goal of this paper is to investigate the methods that are used for efficient grouping of data. The grouping must be done in its manner that the group can recognize the group members and group can also recognize still, not grouped member so far. Different approaches for customer segmentation in data mining are: clustering and subgroup discovery. Because of some limitations and scope of the clustering techniques, it leads to further refinements in methodology in data mining.