Clustering Algorithm for Incomplete Data Sets with Mixed Numeric and Categorical Attributes

The traditional k-prototypes algorithm is well versed in clustering data with mixed numeric and categorical attributes, while it is limited to complete data. In order to handle incomplete data set with missing values, an improved k-prototypes algorithm is proposed in this paper, which employs a new dissimilarity measure for incomplete data set with mixed numeric and categorical attributes and a new approach to select k objects as the initial prototypes based on the nearest neighbors. The improved k-prototypes algorithm can not only cluster incomplete data with no need to impute the missing values, but also avoid randomness in choosing initial prototypes. To illustrate the accuracy of the established algorithm, traditional k-prototypes algorithm and k-prototypes employing the new dissimilarity measure are compared to the improved k-prototypes algorithm by using data from UCI machine learning repository. The experimental results show that the improved k-prototypes algorithm is superior to the other two algorithms with higher clustering accuracy.

[1]  Zhexue Huang,et al.  CLUSTERING LARGE DATA SETS WITH MIXED NUMERIC AND CATEGORICAL VALUES , 1997 .

[2]  Wang Feng-mei,et al.  A Missing Data Imputation Method Based on Neighbor Rules , 2012 .

[3]  Li Tian.  Matrix-Based Computational Method for Upper and Lower Approximations of Rough Sets , 2011 .

[4]  Jos Twisk,et al.  Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis. , 2013, Journal of clinical epidemiology.

[5]  Zhou Chun-guang.  New clustering method of mixed-attribute data , 2013 .

[6]  Zhang Gui-zhu.  Clustering algorithm based on Modified Shuffled Frog Leaping Algorithm and K-means , 2013 .

[7]  GhoshJoydeep,et al.  A study of K-Means-based algorithms for constrained clustering , 2013, IDA 2013.

[8]  Wang Zhen-hua.  A K-prototypes Algorithm Based on Improved Initial Center Points , 2010 .

[9]  Lu Huiling,et al.  Clustering algorithm research advances on data mining , 2012 .

[10]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[11]  Francesc Comellas,et al.  A fast and efficient algorithm to identify clusters in networks , 2010, Appl. Math. Comput..

[12]  Yanchi Liu,et al.  Imputing Missing Values for Mixed Numeric and Categorical Attributes Based on Incomplete Data Hierarchical Clustering , 2011, KSEM.

[13]  Bingzhen Sun,et al.  Probabilistic rough set over two universes and rough entropy , 2012, Int. J. Approx. Reason..

[14]  Sotirios Chatzis,et al.  A fuzzy c-means-type algorithm for clustering of data with mixed numeric and categorical attributes employing a probabilistic dissimilarity functional , 2011, Expert Syst. Appl..