Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning

Wu, Shan; Liew, Alan Wee‐Chung; Yan, Hong; Yang, Mengsu

doi:10.1109/titb.2004.824724

Cited by 89 publications

(61 citation statements)

References 22 publications

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“…The intermediate level explores the relations and interactions between genes under different conditions, and attracts more attention currently. Generally, cluster analysis of gene expression data is composed of two aspects: clustering genes [80], [206], [260], [268], [283], [288] or clustering tissues or experiments [5], [109], [238].…”

Section: Bioinformatics-gene Expression Datamentioning

confidence: 99%

Survey of Clustering Algorithms

Wunsch

2005

IEEE Trans. Neural Netw.

5,064

2,338

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Abstract-Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.Index Terms-Adaptive resonance theory (ART), clustering, clustering algorithm, cluster validation, neural networks, proximity, self-organizing feature map (SOFM).

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Section: Bioinformatics-gene Expression Datamentioning

confidence: 99%

Survey of Clustering Algorithms

Wunsch

2005

IEEE Trans. Neural Netw.

5,064

2,338

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“…If a neuron wins the competition, it tries to represent input patterns as efficiently as possible. A number of variants to overcome the problems such as dead neurons, the number of neurons, and initial conditions have been developed [1], [2], [3], [4], [5] , [6], [7], [8], [9]. However, the focus in competitive learning is on competition between output neurons.…”

Section: A Goal Of the Present Papermentioning

confidence: 99%

Double Competition for Information-Theoretic SOM

Kamimura¹

2014

ijarai

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Abstract-In this paper, we propose a new type of informationtheoretic method for the self-organizing maps (SOM), taking into account competition between competitive (output) neurons as well as input neurons. The method is called "double competition", as it considers competition between outputs as well as input neurons. By increasing information in input neurons, we expect to obtain more detailed information on input patterns through the information-theoretic method. We applied the informationtheoretic methods to two well-known data sets from the machine learning database, namely, the glass and dermatology data sets. We found that the information-theoretic method with double competition explicitly separated the different classes. On the other hand, without considering input neurons, class boundaries could not be explicitly identified. In addition, without considering input neurons, quantization and topographic errors were inversely related. This means that when the quantization errors decreased, topographic errors inversely increased. However, with double competition, this inverse relation between quantization and topographic errors was neutralized. Experimental results show that by incorporating information in input neurons, class structure could be clearly identified without degrading the map quality to severely.

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“…High scale research and development planning were a part of the decision enhancement module. A clustering algorithm specifically made to take care of the complexity of gene data was formulated by Wu et al [28] in 2004. Parmar proposed in [11] the MMR or Min-Min-Roughness algorithm.…”

Section: Introductionmentioning

confidence: 99%

MMeMeR: An Algorithm for Clustering Heterogeneous Data using Rough Set Theory

Tripathy¹,

Goyal²,

Chowdhury³

et al. 2017

IJISA

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Abstract-In recent times enumerable number of clustering algorithms have been developed whose main function is to make sets of objects having almost the same features. But due to the presence of categorical data values, these algorithms face a challenge in their implementation. Also some algorithms which are able to take care of categorical data are not able to process uncertainty in the values and so have stability issues. Thus handling categorical data along with uncertainty has been made necessary owing to such difficulties. So, in 2007 MMR algorithm was developed which was based on basic rough set theory. MMeR was proposed in 2009 which surpassed the results of MMR in taking care of categorical data and it could also handle heterogeneous values as well. SDR and SSDR were postulated in 2011 which were able to handle hybrid data. These two showed more accuracy when compared to MMR and MMeR. In this paper, we further make improvements and conceptualize an algorithm, which we call MMeMeR or Min-Mean-Mean-Roughness. It takes care of uncertainty and also handles heterogeneous data. Standard data sets have been used to gauge its effectiveness over the other methods.

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Cluster Analysis of Gene Expression Data Based on Self-Splitting and Merging Competitive Learning

Cited by 89 publications

References 22 publications

Survey of Clustering Algorithms

Survey of Clustering Algorithms

Double Competition for Information-Theoretic SOM

MMeMeR: An Algorithm for Clustering Heterogeneous Data using Rough Set Theory

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