Clustering mixed datasets is one of the challenging task. Traditional algorithms like k-prototype algorithm is used for mixed dataset, but is limited to only complete datasets. In any dataset missing values are common. To handle such missing values or incomplete mixed datasets we use extended squeezer algorithm, which includes the new dissimilarity measure ISMD that is incomplete set mixed dissimilarity for numerical and categorical attribute values. In this method we consider dissimilarities in the missing values and in this extended squeezer algorithm it not only cluster the incomplete dataset, it also need not to input the missing values and need not to initialize any clusters at the beginning. This method is compared with traditional k-prototype algorithm on benchmark datasets. The experimental results shows that the ISMD using extended squeezer algorithm gives better accuracy than the traditional k-prototype algorithm and also it overcomes the limitation of initial clusters. This method is implemented by using Python programming. The results shows that there is significant improvement in the clustering results.

Incomplete set mixed dissimilarity, k-prototype, extended squeezer algorithm, Python programming

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