Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

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A Comprehensive Survey of Clustering Algorithms

Pattern recognition

This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries to reflect the profile of this area by focusing more on those subjects that have been given more importance in the literature. In this context, most of the paper is devoted to partitional algorithms that look for hard clusterings of data, though overlapping (i.e., soft and fuzzy) approaches are also covered in the paper. The paper is original in what concerns two main aspects. First, it provides an up-to-date overview that is fully devoted to evolutionary algorithms for clustering, is not limited to any particular kind of evolutionary approach, and comprises advanced topics like multiobjective and ensemble-based evolutionary clustering. Second, it provides a taxonomy that highlights some very important aspects in the context of evolutionary data clustering, namely, fixed or variable number of clusters, cluster-oriented or nonoriented operators, context-sensitive or context-insensitive operators, guided or unguided operators, binary, integer, or real encodings, centroid-based, medoid-based, label-based, tree-based, or graph-based representations, among others. A number of references are provided that describe applications of evolutionary algorithms for clustering in different domains, such as image processing, computer security, and bioinformatics. The paper ends by addressing some important issues and open questions that can be subject of future research.

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A Survey of Evolutionary Algorithms for Clustering

Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.

A survey of clustering ensemble algorithms

Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 305–315 DOI: 10.1002/widm.32

Cluster ensembles

Biological data set sizes have been growing rapidly with the technological advances that have occurred in bioinformatics. Data mining techniques have been used extensively as approaches to detect interesting patterns in large databases. In bioinformatics, clustering algorithm technique for data mining can be applied to find underlying genetic and biological interactions, without considering prior information from datasets. However, many clustering algorithms are practically available, and different clustering algorithms may generate dissimilar clustering results due to bio-data characteristics and experimental assumptions. In this paper, we propose a novel heterogeneous clustering ensemble scheme that uses a genetic algorithm to generate high quality and robust clustering results with characteristics of bio-data. The proposed method combines results of various clustering algorithms and crossover operation of genetic algorithm, and is founded on the concept of using the evolutionary processes to select the most commonly-inherited characteristics. Our framework proved to be available on real data set and the optimal clustering results generated by means of our proposed method are detailed in this paper. Experimental results demonstrate that the proposed method yields better clustering results than applying a single best clustering algorithm.

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Heterogeneous clustering ensemble method for combining different cluster results

We propose a novel method, called heterogeneous clustering ensemble (HCE), to generate robust clustering results that combine multiple partitions (clusters) derived from various clustering algorithms. The proposed method combines partitions of various clustering algorithms by means of newly-proposed the selection and the crossover operation of the genetic algorithm (GA) during the evolutionary process.

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A novel framework for discovering robust cluster results

Emerging patterns (EP) represent a class of interaction structures and have recently been proposed as a tool for data mining. Especially, EP have been applied to the production of new types of classifiers during classification in data mining. Traditional clustering and pattern mining algorithms are inadequate for handling the analysis of high dimensional gene expression data or the analysis of multi-source data based on the same variables (e.g. genes), and the experimental results are not easy to understand. In this paper, a simple scheme for using EP to improve the performance of classification procedures in multi-source data is proposed. Also, patterns that make multi-source data easy to understand are obtained as experimental results. A new method for producing EP based on observations (e.g. samples in microarray data) in the search of classification patterns and the use of detected patterns for the classification of variables in multi-source data are presented.

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Application of emerging patterns for multi-source bio-data classification and analysis

In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conducting data analysis using multiple sources, given the complexity of data objects, is a much more powerful method than evaluating each source separately. Therefore, a new paradigm is required that combines the genome-wide experimental results of multi-source datasets. However, multi-source data analysis is more difficult than single source data analysis. In this paper, we propose a new clustering ensemble approach for multi-source bio-data on complex objects. In addition, we present encouraging clustering results in a real bio-dataset examined using our proposed method.

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Hye-Sung Yoon

Papers

Heterogeneous clustering ensemble method for combining different cluster results

A novel framework for discovering robust cluster results

Application of emerging patterns for multi-source bio-data classification and analysis

Integration analysis of diverse genomic data using multi-clustering results