Abstract
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.
Highlights
As an important technique in data mining, clustering analysis has been used in many fields [1,2], such as information retrieval [3], social media analysis [4], privacy preserving [5], image analysis [6], text analysis [7], and bioinformatics [8]
For evaluating the performance of our proposed clustering algorithm artificial bee colony (ABC)-KModes, we run the proposed approach on six real-world categorical datasets: Zoo, Breast cancer, Soybean, Lung cancer, Mushroom, and Dermatology, all of which can be downloaded from UCI Machine Learning Repository
The parameters of the proposed ABC-K-Modes algorithm are set as follows: N = 20, MCN = 1000, which are the typical values used in the original ABC algorithm [30]; L = 5 and T = 5 are set by the rule of thumb
Summary
As an important technique in data mining, clustering analysis has been used in many fields [1,2], such as information retrieval [3], social media analysis [4], privacy preserving [5], image analysis [6], text analysis [7], and bioinformatics [8]. The aim of clustering is to group those data objects with similar characteristics into the same clusters, and the ones with dissimilar characteristics into different clusters. Most existing clustering algorithms in the literature belong to one of the following two types: hierarchical and partitional. Hierarchical clustering algorithms allocate a group of data objects into a dendrogram of the nested partitions according to a divisive or agglomerative strategy [9]. While partitional clustering algorithms partition a set of data objects into a pre-defined number of clusters by optimizing an objective cost function. Center-based clustering algorithms are the most popular partitional clustering algorithms. The k-means algorithm is a widely used center-based partitional clustering algorithm due to its simplicity and high efficiency [10]. Considering the uncertainty of data objects, the fuzzy k-
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