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22nd International Conference on Neural Information Processing (ICONIP)

作者： Piantoni, Jane Faceli, Katti Sakata, Tiemi C. Pereira, Julio C. de Souto, Marcilio C. P. Univ Fed Sao Carlos Sorocaba Brazil Univ Orleans INSA Ctr Val de Loire LIFO EA 4022 Orleans France

ISBN: (纸本)9783319265322;9783319265315

This paper presents a comparative study of cluster ensemble and multi-objective cluster ensemble algorithms. Our aim is to evaluate the extent to which such methods are able to identify the underlying structure hidden in a data set, given different levels of information they receive as input in the set of base partitions (BP). To do so, given a gold/reference partition, we produced nine sets of BP containing properties of interest for our analysis, such as large number of subdivisions of true clusters. We aim at answering questions such as: are the methods able to generate new and more robust partitions than those in the set of BP? are the techniques influenced by poor quality partitions presented in the set of BP?

关键词： clustering Cluster ensemble multi-objective clustering Performance evaluation

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A Unified multi-view clustering Algorithm Using multi-objective Optimization Coupled with Generative Model

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ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2020年第1期14卷 2-2页

作者： Mitra, Sayantan Hasanuzzaman, Mohammed Saha, Sriparna Indian Inst Technol Patna Bihta 801103 Bihar India Dublin City Univ ADAPT Ctr Sch Comp Dublin Ireland

There is a large body of works on multi-view clustering that exploit multiple representations (or views) of the same input data for better convergence. These multiple views can come from multiple modalities (image, audio, text) or different feature subsets. Obtaining one consensus partitioning after considering different views is usually a non-trivial task. Recently, multi-objective based multi-view clustering methods have suppressed the performance of single objective based multi-view clustering techniques. One key problem is that it is difficult to select a single solution from a set of alternative partitionings generated by multi-objective techniques on the final Pareto optimal front. In this article, we propose a novel multi-objective based multi-view clustering framework that overcomes the problem of selecting a single solution in multi-objective based techniques. In particular, our proposed framework has three major components as follows: (i) multi-view based multi-objective algorithm, multiview-AMOSA, for initial clustering of data points;(ii) a generative model for generating a combined solution having probabilistic labels;and (iii) K-means algorithm for obtaining the final labels. As the first component, we have adopted a recently developed multi-view based multi-objective clustering algorithm to generate different possible consensus partitionings of a given dataset taking into account different views. A generative model is coupled with the first component to generate a single consensus partitioning after considering multiple solutions. It exploits the latent subsets of the non-dominated solutions obtained from the multi-objective clustering algorithm and combines them to produce a single probabilistic labeled solution. Finally, a simple clustering algorithm, namely K-means, is applied on the generated probabilistic labels to obtain the final cluster labels. Experimental validation of our proposed framework is carried out over several benchmark datasets bel

关键词： multi-objective clustering multi-view clustering generative model search result clustering

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A survey on nature inspired metaheuristic algorithms for partitional clustering

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 16卷 1-18页

作者： Nanda, Satyasai Jagannath Panda, Ganapati Malaviya Natl Inst Technol Jaipur Dept Elect & Commun Engn Jaipur 302017 Rajasthan India Indian Inst Technol Sch Elect Sci Bhubaneswar 751013 Odisha India

The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed. (C) 2014 Published by Elsevier B.V.

关键词： Partitional clustering Nature inspired metaheuristics Evolutionary algorithms Swarm intelligence multi-objective clustering

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Adaptive Group Teaching Based clustering and Data Aggregation with Routing in Wireless Sensor Network

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WIRELESS PERSONAL COMMUNICATIONS 2022年第2期122卷 1839-1866页

作者： Rajesh, L. Mohan, H. S. SJB Inst Technol Dept Informat Sci & Engn Bengaluru 560060 India

Nowadays, Wireless Sensor Networks (WSNs) is enhancing for different applications. Simultaneously, energy consumption for processing the tasks in most of the applications has also been increased. The nodes in the network may die while utilizing more energy for executing the tasks. The death of nodes causes various issues in data transmission. In most cases, the nodes next to the sink may have more traffic and it makes the energy to drain quickly. Such situation can be handled with an optimization algorithm. In this paper, an energy efficient data aggregation approach is proposed by the combination of multi-objective clustering and optimization-based routing algorithm. Initially, the K-Means clustering technique is employed for cluster formation. Further, cluster head is selected using Adaptive Group Teaching Optimization Algorithm based on multiple objectives mainly considers on reducing the energy usage for preventing nodes from death. A minimum spanning tree algorithm is employed to create the communication route form sink node to cluster head. Finally, the path for transmitting the data to the base station is identified with the Improved Deer Hunting Optimization algorithm. Performance of presented routing algorithm is compared along with existing routing approaches to compare the performance and is found to be performing better than them.

关键词： Energy efficiency Wireless sensor network multi-objective clustering Hierarchical routing Deer hunting optimization

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Refined PSO clustering for Not Well-Separated Data

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2023年第6期35卷 831-847页

作者： Sunny, Chilankamol Kumar, Shibu K. B. Rajiv Gandhi Inst Technol Dept Comp Sci & Engn Kottayam Kerala India

Cluster analysis is the most popular and often the foremost task in big data analytics as it helps in unearthing hidden patterns and trends in data. Traditional single-objective clustering techniques often suffer from accuracy fluctuations especially when applied over data groups of varying densities and imbalanced distribution as well as in the presence of outliers. This paper presents a multi-phase clustering solution that achieves good accuracy measures even in the case of noisy and not- well-separated data (linearly not separable data). The proposed design combines a two-stage Particle Swarm Optimisation (PSO) clustering with K-means logic and a state-of-the-art outlier removal technique. The use of two different optimisation criteria in the two stages of PSO clustering equips the model with the ability to escape local minima traps in the process of convergence. Extensive experiments featuring a wide variety of data have been carried out and the system could achieve accuracy levels as high as 99.9% and an average of 87.4% on notwell-separated data. The model has also been proved to be robust on eight out of the ten datasets of the Fundamental clustering Problem Suit (FCPS), a benchmark for clustering algorithms.

关键词： PSO clustering multi-objective clustering not-well-separated data outlier removal

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multi-Factored Gene-Gene Proximity Measures Exploiting Biological Knowledge Extracted from Gene Ontology: Application in Gene clustering

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第1期17卷 207-219页

作者： Acharya, Sudipta Saha, Sriparna Pradhan, Prasanna Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801103 Bihar India Sikkim Manipal Inst Technol Dept Comp Applicat Rangpo 737132 Sikkim India

To describe the cellular functions of proteins and genes, a potential dynamic vocabulary is Gene Ontology (GO), which comprises of three sub-ontologies namely, Biological-process, Cellular-component, and Molecular-function. It has several applications in the field of bioinformatics like annotating/measuring gene-gene or protein-protein semantic similarity, identifying genes/proteins by their GO annotations for disease gene and target discovery, etc. To determine semantic similarity between genes, several semantic measures have been proposed in literature, which involve information content of GO-terms, GO tree structure, or the combination of both. But, most of the existing semantic similarity measures do not consider different topological and information theoretic aspects of GO-terms collectively. Inspired by this fact, in this article, we have first proposed three novel semantic similarity/distance measures for genes covering different aspects of GO-tree. These are further implanted in the frameworks of well-known multi-objective and single-objective based clustering algorithms to determine functionally similar genes. For comparative analysis, 10 popular existing GO based semantic similarity/distance measures and tools are also considered. Experimental results on Mouse genome, Yeast, and Human genome datasets evidently demonstrate the supremacy of multi-objective clustering algorithms in association with proposed multi-factored similarity/distance measures. clustering outcomes are further validated by conducting some biological/statistical significance tests. Supplementary information is available at https://***/sriparna/***.

关键词： Semantics Integrated circuits Bioinformatics Ontologies Tools Genomics Current measurement Gene ontology (GO) gene clustering semantic similarity distance measure gene-gene similarity matrix multi-objective clustering

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Unsupervised gene selection using biological knowledge : application in sample clustering

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BMC BIOINFORMATICS 2017年第1期18卷 1-13页

作者： Acharya, Sudipta Saha, Sriparna Nikhil, N. IIT Patna Dept Comp Sci & Engn Patna Bihar India IIT Ropar Dept Comp Sci & Engn Rupnagar Punjab India

Background: Classification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role. Results: The current paper explores the use of biological knowledge acquired from Gene Ontology database in selecting the proper subset of genes which can further participate in clustering of samples. The proposed feature selection technique is unsupervised in nature as it does not utilize any class label information in the process of gene selection. At the end, a multi-objective clustering approach is deployed to cluster the available set of samples in the reduced gene space. Conclusions: Reported results show that consideration of biological knowledge in gene selection technique not only reduces the feature space dimensionality in great extent but also improves the accuracy of sample classification. The obtained reduced gene space is validated using strong biological significance tests. In order to prove the supremacy of our proposed gene selection based sample clustering technique, a thorough comparative analysis has also been performed with state-of-the-art techniques.

关键词： Feature selection Gene Ontology (GO) Sample classification Gene-GO term annotation matrix multi-objective clustering

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A joint optimization framework integrated with biological knowledge for clustering incomplete gene expression data

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SOFT COMPUTING 2023年第18期27卷 13639-13656页

作者： Li, Dan Gu, Hong Chang, Qiaozhen Wang, Jia Qin, Pan Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China Dalian Med Univ Dept Breast Surg Hosp 2 Dalian 116023 Peoples R China

clustering algorithms have been successfully applied to identify co-expressed gene groups from gene expression data. Missing values often occur in gene expression data, which presents a challenge for gene clustering. When partitioning incomplete gene expression data into co-expressed gene groups, missing value imputation and clustering are generally performed as two separate processes. These two-stage methods are likely to result in unsuitable imputation values for clustering task and unsatisfying clustering performance. This paper proposes a multi-objective joint optimization framework for clustering incomplete gene expression data that addresses this problem. The proposed framework can impute the missing expression values under the guidance of clustering, and therefore realize the synergistic improvement of imputation and clustering. In addition, gene expression similarity and gene semantic similarity extracted from the Gene Ontology are combined, as the form of functional neighbor interval for each missing expression value, to provide reasonable constraints for the joint optimization framework. The experiments are carried out on several benchmark data sets. In terms of the average improvement rate over the data sets and different missing rates, our framework can reduce the imputation error by 6.4-14.7% and increase the clustering accuracy by 4.0-10.1% compared with six popular and promising methods. Furthermore, biological significance of the identified gene clusters is reported to evaluate the effectiveness of the proposed framework.

关键词： Gene clustering Joint optimization multi-objective clustering Imputation Gene ontology

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Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering

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GENE 2018年 679卷 341-351页

作者： Acharya, Sudipta Saha, Sriparna Pradhan, Prasanna IIT Patna Dept Comp Sci & Engn Dealpur Daulat Bihar India Sikkim Manipal Inst Technol Dept Comp Applicat Majitar Sikkim India

In recent years DNA microarray technology, leading to the generation of high-volume biological data, has gained significant attention. To analyze this high volume gene-expression data, one such powerful tool is clustering. For any clustering algorithm, its efficiency majorly depends upon the underlying similarity/dissimilarity measure. During the analysis of such data often there is a need to further explore the similarity of genes not only with respect to their expression values but also with respect to their functional annotations, which can be obtained from Gene Ontology (GO) databases. In the existing literature, several novel clustering and bi-clustering approaches were proposed to identify co-regulated genes from gene-expression datasets. Identifying co-regulated genes from gene expression data misses some important biological information about functionalities of genes, which is necessary to identify semantically related genes. In this paper, we have proposed sixteen different semantic gene-gene dissimilarity measures utilizing biological information of genes retrieved from a global biological database namely Gene Ontology (GO). Four proximity measures, viz. Euclidean, Cosine, point symmetry and line symmetry are utilized along with four different representations of gene-GO-term annotation vectors to develop total sixteen gene-gene dissimilarity measures. In order to illustrate the profitability of developed dissimilarity measures, some multi-objective as well as single-objective clustering algorithms are applied utilizing proposed measures to identify functionally similar genes from Mouse genome and Yeast datasets. Furthermore, we have compared the performance of our proposed sixteen dissimilarity measures with three existing state-of-the-art semantic similarity and distance measures.

关键词： Gene Ontology(GO) Dissimilarity measure Symmetry-based distance Gene clustering Gene-GO term annotation matrix multi-objective clustering

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On Evolving Neighborhood Parameters for Fuzzy Density clustering

On Evolving Neighborhood Parameters for Fuzzy Density Cluste...

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IEEE Congress on Evolutionary Computation

作者： Banerjee, Amit Penn State Univ Harrisburg Sch Sci Engn & Technol Middletown PA 17057 USA

ISBN: (纸本)9781479904549;9781479904532

The problem of identifying core patterns with the correct neighborhood parameters is a major challenge for density-based clustering techniques derived from the popular DBSCAN algorithm. An evolutionary approach to optimizing the assignment of core patterns is proposed in this paper. Key ideas presented here include a genetic representation that associates distinct neighborhood parameters with potential core patterns and specialized crossover and mutation operators. The evolutionary framework is based on the multi-objective NSGA-II algorithm, with simplified fitness measures derived from local (neighborhood) information. clustering experiments on both synthetic and benchmark clustering datasets are presented and results are compared to the original DBSCAN, fuzzy DBSCAN and k-means.

关键词： fuzzy density clustering evolutionary clustering multi-objective clustering DBSCAN neighborhood parameters

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