Gradual representation of shadowed set for clustering gene expression data

Micro array has been a widely used microscopic measurement that accumulates the expression levels of a large number of genes varying over different time points. Cluster analysis more over the concept of bi-clustering provides insight into meaningful information from the correlation of a subset of genes with a subset of conditions. This eventually helps in discovering biologically meaningful clusters over analyzing missing values, imprecision and noise present in micro array data set. Although the concept of fuzzy set is enough to deal with the overlapping nature of the bi-clusters but the use of shadowed set helps in identifying and analyzing the nature of the genes lying in the confusion area of the clusters. In this article, we have suggested a bi-clustering model of the shadowed set with gradual representation of cardinality and named it as Gradual shadowed set for gene expression (GSS-GE) clustering. It identifies the bi-clusters in the core and in the shadowed region and evaluates their biological significance. The excellence of the proposed GSS-GE has been demonstrated by considering three real data sets, namely yeast data, serum data and mouse data set. The performance is compared with Ching Church’s algorithm (CC), Bimax, order preserving sub matrix (OPSM), Large Average Sub matrices (LAS), statistical plaid model and a modified fuzzy co-clustering (MFCC) algorithm. For the mouse data set there is no cluster level analysis of the micro array has been done so far. We have also provided the statistical and biological significance to prove the superiority of the proposed GSS-GE.     

基因微阵列数据的聚类分析算法研究

微阵列技术是后基因组时代功能基因组研究的主要工具。基因表达谱数据的聚类分析对于研究基因功能和基因调控机制有重要意义。针对聚类算法要求事先确定簇的个数、对噪声敏感和可伸缩性差的问题,基于密度聚类算法DBSCAN和共享近邻SharedNearestNeighbors(SNN)的不同的特点,提出了一种新的最近邻先吸收的聚类算法,将其应用于一个公开的酵母细胞同期数据集,并用评价方法FOM将聚类结果与K-means聚类方法的结果进行了比较。结果表明,该文的聚类算法优于其他聚类算法,聚类结果具有明显的生物学意义,并能对数据的类别数作出较好的预测和评估。     

A Novel Soft Clustering Approach for Gene Expression Data

Gene expression data represents a condition matrix where each row represents the gene and the column shows the condition. Micro array used to detect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messenger RNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with the volume of data containing imprecision and outliers increases the challenges in dealing with them. Clustering methods are hence essential to identify the patterns present in massive gene data. Many techniques involve hierarchical, partitioning, grid based, density based, model based and soft clustering approaches for dealing with the gene expression data. Understanding the gene regulation and other useful information from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate on providing a soft clustering approach for analyzing the gene expression data. The population elements are grouped based on the fuzziness principle and a degree of membership is assigned to all the elements. An improved Fuzzy clustering by Local Approximation of Memberships (FLAME) is proposed in this work which overcomes the limitations of the other approaches while dealing with the non-linear relationships and provide better segregation of biological functions.     

On biological validity indices for soft clustering algorithms for gene expression data

Unsupervised clustering methods such as K-means, hierarchical clustering and fuzzy c-means have been widely applied to the analysis of gene expression data to identify biologically relevant groups of genes. Recent studies have suggested that the incorporation of biological information into validation methods to assess the quality of clustering results might be useful in facilitating biological and biomedical knowledge discoveries. In this study, we generalize two bio-validity indices, the biological homogeneity index and the biological stability index, to quantify the abilities of soft clustering algorithms such as fuzzy c-means and model-based clustering. The results of an evaluation of several existing soft clustering algorithms using simulated and real data sets indicate that the soft versions of the indices provide both better precision and better accuracy than the classical ones. The significance of the proposed indices is also discussed.     

Use of SVD-based probit transformation in clustering gene expression profiles

The mixture-Gaussian model-based clustering method has received much attention in clustering gene expression profiles in the literature of bioinformatics. However, this method suffers from two difficulties in applications. The first one is on the parameter estimation, which becomes difficult when the dimension of the data is high or the size of a cluster is small. The second one is on the normality assumption for gene expression levels, which is seldom satisfied by real data. In this paper, we propose to overcome these two difficulties by the probit transformation in conjunction with the singular value decomposition (SVD). SVD reduces the dimensionality of the data, and the probit transformation converts the scaled eigensamples, which can be interpreted as correlation coefficients as explained in the text, into Gaussian random variables. Our numerical results show that the SVD-based probit transformation enhances the ability of the mixture-Gaussian model-based clustering method for identifying prominent patterns of the data. As a by-product, we show that the SVD-based probit transformation also improves the performance of the model-free clustering methods, such as hierarchical, K-means and self-organizing maps (SOM), for the data sets containing scattered genes. In this paper, we also propose a run test-based rule for selection of eigensamples used for clustering.     

Cluster analysis for gene expression data: a survey

DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increases the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expression data, and also new algorithms have recently been proposed specifically aiming at gene expression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. In particular, we divide cluster analysis for gene expression data into three categories. Then, we present specific challenges pertinent to each clustering category and introduce several representative approaches. We also discuss the problem of cluster validation in three aspects and review various methods to assess the quality and reliability of clustering results. Finally, we conclude this paper and suggest the promising trends in this field.     

Clustering Gene Expression Data Through Modified Agglomerative M-CURE Hierarchical Algorithm

Gene expression refers to the process in which the gene information is used in the functional gene product synthesis. They basically encode the proteins which in turn dictate the functionality of the cell. The first step in gene expression study involves the clustering usage. This is due to the reason that biological networks are very complex and the genes volume increases the comprehending challenges along with the data interpretation which itself inhibit vagueness, noise and imprecision. For a biological system to function, the essential cellular molecules must interact with its surrounding including RNA, DNA, metabolites and proteins. Clustering methods will help to expose the structures and the patterns in the original data for taking further decisions. The traditional clustering techniques involve hierarchical, model based, partitioning, density based, grid based and soft clustering methods. Though many of these methods provide a reliable output in clustering, they fail to incorporate huge data of gene expressions. Also, there are statistical issues along with choosing the right method and the choice of dissimilarity matrix when dealing with gene expression data. We propose to use a modified clustering algorithm using representatives (M-CURE) in this work which is more robust to outliers as compared to K-means clustering and also able to find clusters with size variances.     

Towards improving fuzzy clustering using support vector machine: Application to gene expression data

Recent advancement in microarray technology permits monitoring of the expression levels of a large set of genes across a number of time points simultaneously. For extracting knowledge from such huge volume of microarray gene expression data, computational analysis is required. Clustering is one of the important data mining tools for analyzing such microarray data to group similar genes into clusters. Researchers have proposed a number of clustering algorithms in this purpose. In this article, an attempt has been made in order to improve the performance of fuzzy clustering by combining it with support vector machine (SVM) classifier. A recently proposed real-coded variable string length genetic algorithm based clustering technique and an iterated version of fuzzy C-means clustering have been utilized in this purpose. The performance of the proposed clustering scheme has been compared with that of some well-known existing clustering algorithms and their SVM boosted versions for one simulated and six real life gene expression data sets. Statistical significance test based on analysis of variance (ANOVA) followed by posteriori Tukey-Kramer multiple comparison test has been conducted to establish the statistical significance of the superior performance of the proposed clustering scheme. Moreover biological significance of the clustering solutions have been established.     

Impact of Fuzzy Normalization on Clustering Microarray Temporal Datasets Using Cuckoo Search

Microarrays have reformed biotechnological research in the past decade. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks with larger volume of genes also increases the challenges of comprehending and interpretation of the resulting mass of data. Clustering addresses these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and molecular functions. Clustering techniques are used to examine gene expression data to extract groups of genes from the tested samples based on a similarity criterion. Subspace clustering broadens the traditional clustering by extracting the groups of genes that are highly correlated in different subspace within the dataset. Mining the temporal patterns in high dimensional data is done with computational effort and thus normalization is needed. In this work, normalization using fuzzy logic is applied to the data before clustering. The multi-objective cuckoo search optimization is implemented to extract co-expressed genes over different subspaces. The proposed methods are applied to the real life temporal gene expression datasets in which it extracts the genes that are responsible for the disease grouped in a same cluster. The experiment results prove that the impact of fuzzy normalization on the dataset improves the clustering.     

A novel incremental conceptual hierarchical text clustering method using CFu-tree

As a data mining method, clustering, which is one of the most important tools in information retrieval, organizes data based on unsupervised learning which means that it does not require any training data. But, some text clustering algorithms cannot update existing clusters incrementally and, instead, have to recompute a new clustering from scratch. In view of above, this paper presents a novel down-top incremental conceptual hierarchical text clustering approach using CFu-tree (ICHTC-CF) representation, which starts with each item as a separate cluster. Term-based feature extraction is used for summarizing a cluster in the process. The Comparison Variation measure criterion is also adopted for judging whether the closest pair of clusters can be merged or a previous cluster can be split. And, our incremental clustering method is not sensitive to the input data order. Experimental results show that the performance of our method outperforms k-means, CLIQUE, single linkage clustering and complete linkage clustering, which indicate our new technique is efficient and feasible.     

An evolutionary computational model applied to cluster analysis of DNA microarray data

This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of DNA microarray data.     

SignatureClust: a tool for landmark gene-guided clustering

Over the last several years, many clustering algorithms have been applied to gene expression data. However, most clustering algorithms force the user into having one set of clusters, resulting in a restrictive biological interpretation of gene function. It would be difficult to interpret the complex biological regulatory mechanisms and genetic interactions from this restrictive interpretation of microarray expression data. The software package SignatureClust allows users to select a group of functionally related genes (called ‘Landmark Genes’), and to project the gene expression data onto these genes. Compared to existing algorithms and software in this domain, our software package offers two unique benefits. First, by selecting different sets of landmark genes, it enables the user to cluster the microarray data from multiple biological perspectives. This encourages data exploration and discovery of new gene associations. Second, most packages associated with clustering provide internal validation measures, whereas our package validates the biological significance of the new clusters by retrieving significant ontology and pathway terms associated with the new clusters. SignatureClust is a free software tool that enables biologists to get multiple views of the microarray data. It highlights new gene associations that were not found using a traditional clustering algorithm. The software package ‘SignatureClust’ and the user manual can be downloaded from .     

ICEAGE: Interactive Clustering and Exploration of Large and High-Dimensional Geodata

The unprecedented large size and high dimensionality of existing geographic datasets make the complex patterns that potentially lurk in the data hard to find. Clustering is one of the most important techniques for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods focus on the specific characteristics of distributions in 2- or 3-D space, while general-purpose high-dimensional clustering methods have limited power in recognizing spatial patterns that involve neighbors. Second, clustering methods in general are not geared toward allowing the human-computer interaction needed to effectively tease-out complex patterns. In the current paper, an approach is proposed to open up the black box of the clustering process for easy understanding, steering, focusing and interpretation, and thus to support an effective exploration of large and high dimensional geographic data. The proposed approach involves building a hierarchical spatial cluster structure within the high-dimensional feature space, and using this combined space for discovering multi-dimensional (combined spatial and non-spatial) patterns with efficient computational clustering methods and highly interactive visualization techniques. More specifically, this includes the integration of: (1) a hierarchical spatial clustering method to generate a 1-D spatial cluster ordering that preserves the hierarchical cluster structure, and (2) a density- and grid-based technique to effectively support the interactive identification of interesting subspaces and subsequent searching for clusters in each subspace. The implementation of the proposed approach is in a fully open and interactive manner supported by various visualization techniques.     

基于计算智能的基因表达数据聚类分析研究进展

基因表达数据聚类是发现基因功能和确立基因调控网络的重要方法,计算智能在该领域的应用为分析 大量基因数据提供了新途径．本文根据基因表达数据的特点,提出了基因表达数据聚类领域的关键问题,探讨了基 于计算智能的基因表达数据聚类基本框架,综述了计算智能在基因数据聚类领域的应用现状,最后指出了在基因数 据聚类领域计算智能方法未来的发展方向．     

一种新聚类算法在基因表达数据分析中的应用

自组织特征映射神经网络与层次聚类算法是两种较经典的分析基因表达数据的聚类算法,但由于基因表达数据的复杂性与不稳定性,这两种算法都存在着自身的优劣。因此,在比较两种算法差异性的基础上,创造性地提出了一种新算法,即通过SOM算法对基因表达数据进行聚类,再用层次聚类将每个类对应的神经元权值二次聚类,并将此算法应用在酵母菌基因表达数据中,用实验证明改进算法克服了自组织算法的一些缺陷,提高了基因聚类的效能。     

An evolutionary clustering algorithm for gene expression microarray data analysis

Clustering is concerned with the discovery of interesting groupings of records in a database. Many algorithms have been developed to tackle clustering problems in a variety of application domains. In particular, some of them have been used in bioinformatics research to uncover inherent clusters in gene expression microarray data. In this paper, we show how some popular clustering algorithms have been used for this purpose. Based on experiments using simulated and real data, we also show that the performance of these algorithms can be further improved. For more effective clustering of gene expression microarray data, which is typically characterized by a lot of noise, we propose a novel evolutionary algorithm called evolutionary clustering (EvoCluster). EvoCluster encodes an entire cluster grouping in a chromosome so that each gene in the chromosome encodes one cluster. Based on such encoding scheme, it makes use of a set of reproduction operators to facilitate the exchange of grouping information between chromosomes. The fitness function that the EvoCluster adopts is able to differentiate between how relevant a feature value is in determining a particular cluster grouping. As such, instead of just local pairwise distances, it also takes into consideration how clusters are arranged globally. Unlike many popular clustering algorithms, EvoCluster does not require the number of clusters to be decided in advance. Also, patterns hidden in each cluster can be explicitly revealed and presented for easy interpretation even by casual users. For performance evaluation, we have tested EvoCluster using both simulated and real data. Experimental results show that it can be very effective and robust even in the presence of noise and missing values. Also, when correlating the gene expression microarray data with DNA sequences, we were able to uncover significant biological binding sites (both previously known and unknown) in each cluster discovered by EvoCluster.     

层次非负矩阵分解及在文本聚类中的应用

文本聚类的目标是把数据集中内容相似的文档归为一类,而使内容不同的文档分开。目前针对不同领域的需求,多种解决聚类问题的算法应运而生。然而,由于文本数据本身固有的复杂特点,如海量、高维、稀疏等,使得对海量文本数据的聚类仍然是一个棘手的问题。提出了层次非负矩阵分解聚类方法,该方法不但保留了非负矩阵分解的优点,如同步识别文档类别和找出类别本质特征,而且能够展现类别间的层次结构。这种类别层次结构在网页预览等应用中是非常有用的。在真实数据集20Newsgroups和Reuters-RCV1上的实验结果表明,层次非负矩阵分解相比已有的方法更有效。     

一种增强的基于GCA的入侵检测方法

增强的基于GCA（Gravity-based clustering approach）的入侵检测方法是先对训练集采用GCA进行聚类,然后依据凝聚层次聚类算法的思想,以簇间的差异度和整体相似度作为聚类质量评价标准对GCA聚类产生的簇进行一些合并,合并后能使簇中心更集中,簇内对象更紧密。再根据标记算法标记出哪些簇属于正常簇,哪些属于异常簇,最后用检测算法对测试集数据进行检测。实验表明该方法对未知攻击的检测能力有所增强,特别是能有效降低误报率。     

Constrained data clustering by depth control and progressive constraint relaxation

In order to import the domain knowledge or application-dependent parameters into the data mining systems, constraint-based mining has attracted a lot of research attention recently. In this paper, the attributes employed to model the constraints are called constraint attributes and those attributes involved in the objective function to be optimized are called optimization attributes. The constrained clustering considered in this paper is conducted in such a way that the objective function of optimization attributes is optimized subject to the condition that the imposed constraint is satisfied. Explicitly, we address the problem of constrained clustering with numerical constraints, in which the constraint attribute values of any two data items in the same cluster are required to be within the corresponding constraint range. This numerical constrained clustering problem, however, cannot be dealt with by any conventional clustering algorithms. Consequently, we devise several effective and efficient algorithms to solve such a clustering problem. It is noted that due to the intrinsic nature of the numerical constrained clustering, there is an order dependency on the process of attaining the clustering, which in many cases degrades the clustering results. In view of this, we devise a progressive constraint relaxation technique to remedy this drawback and improve the overall performance of clustering results. Explicitly, by using a smaller (tighter) constraint range in earlier iterations of merge, we will have more room to relax the constraint and seek for better solutions in subsequent iterations. It is empirically shown that the progressive constraint relaxation technique is able to improve not only the execution efficiency but also the clustering quality.     

A mathematical morphology based method for hierarchical clustering analysis of spatial points on street networks

Spatial clustering analysis is an important issue that has been widely studied to extract the meaningful subgroups of geo-referenced data. Although many approaches have been developed in the literature, efficiently modeling the network constraint that objects (e.g. urban facility) are observed on or alongside a street network remains a challenging task for spatial clustering. Based on the techniques of mathematical morphology, this paper presents a new spatial clustering approach NMMSC designed for mining the grouping patterns of network-constrained point objects. NMMSC is essentially a hierarchical clustering approach, and it generally consists of two main steps: first, the original vector data is converted to raster data by utilizing basic linear unit of network as the pixel in network space; second, based on the specified 1-dimensional raster structure, an extended mathematical morphology operator (i.e. dilation) is iteratively performed to identify spatial point agglomerations with hierarchical structure snapped on a network. Compared to existing methods of network-constrained hierarchical clustering, our method is more efficient for cluster similarity computation with linear time complexity. The effectiveness and efficiency of our approach are verified through the experiments with real and synthetic data sets.