nchao Ji

A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data

In many applications, data objects are described by both numeric and categorical features. The k-prototype algorithm is one of the most important algorithms for clustering this type of data. However, this method performs hard partition, which may lead to misclassification for the data objects in the boundaries of regions, and the dissimilarity measure only uses the user-given parameter for adjusting the significance of attribute. In this paper, first, we combine mean and fuzzy centroid to represent the prototype of a cluster, and employ a new measure based on co-occurrence of values to evaluate the dissimilarity between data objects and prototypes of clusters. This measure also takes into account the significance of different attributes towards the clustering process. Then we present our algorithm for clustering mixed data. Finally, the performance of the proposed method is demonstrated by a series of experiments on four real world datasets in comparison with that of traditional clustering algorithms.

An improved k-prototypes clustering algorithm for mixed numeric and categorical data

Abstract Data objects with mixed numeric and categorical attributes are commonly encountered in real world. The k-prototypes algorithm is one of the principal algorithms for clustering this type of data objects. In this paper, we propose an improved k-prototypes algorithm to cluster mixed data. In our method, we first introduce the concept of the distribution centroid for representing the prototype of categorical attributes in a cluster. Then we combine both mean with distribution centroid to represent the prototype of the cluster with mixed attributes, and thus propose a new measure to calculate the dissimilarity between data objects and prototypes of clusters. This measure takes into account the significance of different attributes towards the clustering process. Finally, we present our algorithm for clustering mixed data, and the performance of our method is demonstrated by a series of experiments on four real-world datasets in comparison with that of traditional clustering algorithms.

A Novel Artificial Bee Colony Based Clustering Algorithm for Categorical Data

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

An Initialization Method for Clustering Mixed Numeric and Categorical Data Based on the Density and Distance

Most of the initialization approaches are dedicated to the partitional clustering algorithms which process categorical or numerical data only. However, in real-world applications, data objects with both numeric and categorical features are ubiquitous. The coexistence of both categorical and numerical attributes make the initialization methods designed for single-type data inapplicable to mixed-type data. Furthermore, to the best of our knowledge, in the existing partitional clustering algorithms designed for mixed-type data, the initial cluster centers are determined randomly. In this paper, we propose a novel initialization method for mixed data clustering. In the proposed method, both the distance and density are exploited together to determine initial cluster centers. The performance of the proposed method is demonstrated by a series of experiments on three real-world datasets in comparison with that of traditional initialization methods.

A new Community Detection algorithm based on Distance Centrality

Community detection is important for many complex network applications. A major challenge lies in that the number of communities in a given social network is usually unknown. This paper presents a new community detection algorithm-Distance Centrality based Community Detection (DCCD). The proposed method is capable of detecting the community of network without a preset community number. The method has two components. First we choose the initial center nodes by calculating the centrality of each node using their distance information. Then we measure the similarity between the center nodes and each other nodes in the network, and assign each node to the most similar community. We demonstrate that the proposed distance centrality based community detection algorithm terminated on a good community number, and also has comparable detection accuracy with other existing approaches.

Corrigendum: Corrigendum to 'A fuzzy k-prototype clustering algorithm for mixed numeric and categorical data' [Knowledge-Based Systems, 30 (2012) 129-135]

The authors regret that in the above published paper the following errors occurred: in Section 4.3 of our paper, when introducing the idea of ‘‘Distance and Significance’’ and its relevant definitions as well as their explanations, we did not add the appropriate citing and mention the following paper (Ref. [2] in our paper), which originally proposed the idea and these definitions as well as their explanations: