[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: 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.
Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings; 01/2006
[Show abstract][Hide abstract] ABSTRACT: 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.
Biological and Medical Data Analysis, 7th International Symposium, ISBMDA 2006, Thessaloniki, Greece, December 7-8, 2006, Proceedings; 01/2006
[Show abstract][Hide abstract] ABSTRACT: 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.
Advances in Natural Computation, First International Conference, ICNC 2005, Changsha, China, August 27-29, 2005, Proceedings, Part I; 01/2005