Abstract: With the easy generation and acquisition of data in medical, management, financial, and other fields, a large amount of data with mixed attributes is generated. How to mine valuable information from these kinds of data has attracted the attention of researchers. Clustering is one of the famous data mining methods, which can be employed to find information from the mixed attribute data sets. Various mixed-type data clustering methods have been designed, which can be divided into general clustering algorithms and evolutionary computation-based clustering algorithms. Among them, the evolutionary computation-based clustering algorithms mainly include single-objective or multi-objective optimization algorithms. These proposed algorithms show good performance under the specific context. However, when facing automatic clustering, high dimensional clustering, and multi-objective clustering problems, the algorithms in the first category cannot get satisfying clustering results; on the contrary, the algorithms in the second category show great potential. Therefore, the researchers have conducted in-depth research on the algorithms in the second category. When using the evolutionary computation-based clustering algorithms, two issues need to be taken into consideration further.On the one hand, these algorithms are proposed based on the K-prototype. It is well recognized that K-prototype employs the Hamming distance to compute the similarity of categorical attributes so that it cannot show the true relations between data samples. On the other hand, these algorithms mainly focus on the genetic algorithm, other evolutionary computation-based algorithms, such as bacterial foraging optimization algorithm, are worth studying in solving mixed-type data clustering problems.

Key words: mixed attribute data clustering; bacterial foraging optimization algorithm; multi-objective optimization; multi-learning strategy