A grid-based approach to exploratory data analysis

被引:0
作者
Elliott, Steven D. [1 ]
机构
[1] Arizona State Univ, Tempe, AZ USA
关键词
cluster analysis; data mining; exploratory data analysis; grid-based; market research;
D O I
10.1177/14707853221093515
中图分类号
F [经济];
学科分类号
02 ;
摘要
A grid-based approach to exploratory data analysis is described wherein the size of a grid's cells may be altered. The primary tool of exploratory data analysis described here is cluster analysis. Background information is provided regarding the different types of clustering algorithms and their effectiveness. A grid-based approach for exploratory and cluster analysis is then described. Both exploratory and cluster analyses are performed for a series of different sizes of grid cells. Two marketing data sets are utilized to illustrate research questions often faced by market researchers. The first is a cluster analysis of cities used to create strata for selecting a representative probability sample. The second example is a segmentation of customers into groups for the purpose of targeting advertising and product insight. These examples illustrate related issues such as the elimination of "background noise" observations, significance tests for departures from uniform density, and metrics for the shape of a cluster.
引用
收藏
页码:727 / 737
页数:11
相关论文
共 19 条
  • [1] Aggarwal CC, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P61, DOI 10.1145/304181.304188
  • [2] Aggarwal CC, 2000, SIGMOD REC, V29, P70, DOI 10.1145/335191.335383
  • [3] Automatic subspace clustering of high dimensional data
    Agrawal, R
    Gehrke, J
    Gunopulos, D
    Raghavan, P
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2005, 11 (01) : 5 - 33
  • [4] Berchtold S., 1997, Proceedings of the Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1997, P78, DOI 10.1145/263661.263671
  • [5] REVIEW OF CLASSIFICATION
    CORMACK, RM
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-GENERAL, 1971, 134 : 321 - +
  • [6] Domeniconi C, 2004, SIAM PROC S, P517
  • [7] Fast and robust general purpose clustering algorithms
    Estivill-Castro, V
    Yang, J
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2004, 8 (02) : 127 - 150
  • [8] CLUSTER-ANALYSIS
    EVERITT, B
    [J]. QUALITY & QUANTITY, 1980, 14 (01) : 75 - 100
  • [9] Fielding AH, 2007, CLUSTER AND CLASSIFICATION TECHNIQUES FOR THE BIOSCIENCES, P1, DOI 10.2277/ 0521618002
  • [10] Fraley C, 2007, J CLASSIF, V24, P155, DOI [10.1007/s00357-007-0004-5, 10.1007/s00357-007-0004-z]