MMDBC: Density-based Clustering Algorithm for Mixed Attributes and Multi-dimension Data

A large number of mixed attributes and multi-dimensional data that contain categorical attributes and numerical attributes have become ubiquitous in the real world. In this paper we present the approach which extends the density-based clustering algorithm to categorical domains and domains with mixed numeric and categorical attributes data. Here, datasets are classified three types according to the proportion of attributes, they will be divided into categorical priority, numerical priority, and equivalent priority. It builds mathematical model individually according to different situations. Our approach produces the ultimate result exactly after executing all range queries on numeric data and merging the weight of categorical attributes. Eventually, we use real-dataset, KDD CUP-99 and Adult datasets to demonstrate the clustering performance. The results also show that new approach has the characteristics of fast, efficient and robust.