Geodesic
Improving Categorical Data Clustering Algorithm by Weighting Uncommon Attribute Value Matches
Computer Science and Information Systems, Tome 3 (2006) no. 1.

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This paper presents an improved Squeezer algorithm for categorical data clustering by giving greater weight to uncommon attribute value matches in similarity computations. Experimental results on real life datasets show that, the modified algorithm is superior to the original Squeezer algorithm and other clustering algorithm with respect to clustering accuracy. 
@article{CSIS_2006_3_1_a2,
     author = {Zengyou He and Xiaofei Xu and Shenchun Deng},
     title = {Improving {Categorical} {Data} {Clustering} {Algorithm} by {Weighting} {Uncommon} {Attribute} {Value} {Matches}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {3},
     number = {1},
     year = {2006},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2006_3_1_a2/}
}
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Zengyou He; Xiaofei Xu; Shenchun Deng. Improving Categorical Data Clustering Algorithm by Weighting Uncommon Attribute Value Matches. Computer Science and Information Systems, Tome 3 (2006) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2006_3_1_a2/