A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Motivation: Clustering analysis of data from DNA microarra yh ybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. Results: Am ajor problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m .W eshow that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m .B ysetting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. Availability: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/ <s> BIB001 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Abstract Clustering is a branch of multivariate analysis that is used to create groups of data. While there are currently a variety of techniques that are used for creating clusters, many require defining additional information, including the actual number of clusters, before they can be carried out. The case study of this research presents a novel neural network that is capable of creating groups by using a combination of hierarchical clustering and self-organizing maps, without requiring the number of existing clusters to be specified beforehand. Key words: Clustering, SOM, hierarchical clustering, PAM, Dendrogram, 1. Introduction Cluster analysis is a branch of multivariate statistical analysis that is used for detecting patterns in the classification of elements. Cluster analysis is used in a wide variety of fields including bioinformatics [9] [19] and surveillance [14] [15]. The methods used for clustering differ considerably according to the type of data and the amount of available information. Clustering techniques are typically broken down into the following categories [18] [19] <s> BIB002 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> The Fuzzy c-means is one of the most popular ongoing area of research among all types of researchers including Computer science, Mathematics and other areas of engineering, as well as all areas of optimization practices. Several problems from various areas have been effectively solved by using FCM and its different variants. But, for efficient use of the algorithm in various diversified applications, some modifications or hybridization with other algorithms are needed. A comprehensive survey on FCM and its applications in more than one decade has been carried out in this paper to show the efficiency and applicability in a mixture of domains. Also, another intention of this survey is to encourage new researchers to make use of this simple algorithm (which is popularly called soft classification model) in problem solving. <s> BIB003 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> In the bioinformatics and clinical research areas, microarray technology has been widely used to distinguish a cancer dataset between normal and tumour samples. However, the high dimensionality of gene expression data affects the classification accuracy of an experiment. Thus, feature selection is needed to select informative genes and remove non-informative genes. Some of the feature selection methods, yet, ignore the interaction between genes. Therefore, the similar genes are clustered together and dissimilar genes are clustered in other groups. Hence, to provide a higher classification accuracy, this research proposed k-means clustering and infinite feature selection for identifying informative genes in the selected subset. This research has been applied to colorectal cancer and small round blue cell tumors datasets. Eventually, this research successfully obtained higher classification accuracy than the previous work. <s> BIB004 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Discovering the similar groups is a popular primary step in analysis of biomedical data, which cannot be identified manually. Many supervised and unsupervised machine learning and statistical approaches have been developed to solve this problem. Clustering is an unsupervised learning approach, which organizes the data into similar groups, and is used to discover the intrinsic hidden structure of data. In this paper, we used clustering by fast search and find of density peaks (CDP) approach for cancer subtyping and identification of normal tissues from tumor tissues. In additional, we also address the preprocessing and underlying distance matrix's impact on finalized groups. We have performed extensive experiments on real-world and synthetic cancer gene expression microarray data sets and compared obtained results with state-of-the-art clustering approaches. <s> BIB005 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Kmeans is one of the most-efficient hard-clustering algorithms. It has been successfully applied to a number of problems. However, the efficiency of kmeans is dependent on its initialization of cluster centres. Different swarm intelligence techniques are applied for clustering problem. In this work, we have considered Ant Lion Optimization (ALO) which is a stochastic global optimization models. In this work Kmeans has been integrated with ALO for optimal clustering. The statistical measures of different performance metrics has been calculated and compared. The proposed method performs preferably better than Kmeans and PSO-Kmeans in terms of sum of intracluster distances and F-measure. <s> BIB006 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Because the K-Means algorithm is easy to fall into the local optimum and the Cuckoo search (CS) algorithm is affected by the step size, this paper proposes a K-Means clustering algorithm based on improved cuckoo search (ICS-Kmeans). The algorithm is compared with the original K-means, the Kmeans algorithm based on particle swarm optimization (PSO-Kmeans) and the K-Means algorithm based on the cuckoo search (CS-Kmeans). The experimental results show that the proposed algorithm can obtain better clustering effect, faster convergence rate and better accuracy rate through the experimental test on the UCI standard data set. The algorithm is also applied to the clustering of the characteristic parameters of the heart sound MFCC. The results show that a better clustering center can be obtained, the algorithm converges fast. <s> BIB007 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Abstract With the increasing penetration of security threats, the severity of their impact on the underlying network has increased manifold. Hence, a robust anomaly detection technique, Fuzzified Cuckoo based Clustering Technique (F-CBCT), is proposed in this paper which operates in two phases: training and detection. The training phase is supported using Decision Tree followed by an algorithm based on hybridization of Cuckoo Search Optimization and K-means clustering. In the designed algorithm, a multi-objective function based on Mean Square Error and Silhouette Index is employed to evaluate the two simultaneous distance functions namely-Classification measure and Anomaly detection measure. Once the system is trained, detection phase is initiated in which a fuzzy decisive approach is used to detect anomalies on the basis of input data and distance functions computed in the previous phase. Experimental results in terms of detection rate (96.86%), false positive rate (1.297%), accuracy (97.77%) and F-Measure (98.30%) prove the effectiveness of the proposed model. <s> BIB008 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Fuzzy C Means (FCM) Partitioning <s> Abstract K-Means is a popular cluster analysis method which aims to partition a number of data points into K clusters. It has been successfully applied to a number of problems. However, the efficiency of K-Means depends on its initialization of cluster centers. Different swarm intelligence techniques are applied to clustering problem for enhancing the performance. In this work a hybrid clustering approach based on K-means and Ant Lion Optimization has been considered for optimal cluster analysis. Ant Lion Optimization (ALO) is a stochastic global optimization model. The performance of the proposed algorithm is compared against the performance of K-Means, KMeans-PSO, KMeans-FA, DBSCAN and Revised DBSCAN clustering methods based on different performance metrics. Experimentation is performed on eight datasets, for which the statistical analysis is carried out. The obtained results indicate that the hybrid of K-Means and Ant Lion Optimization method performs preferably better than the other three algorithms in terms of sum of intracluster distances and F-measure. <s> BIB009
Minimize the error function belonging to its objective function and solve the partition factor of the classes. Unable to achieve high convergence. BIB001 BIB003 K-means Clustering Partitioning Use a minimum "within-class sum of squares from the centers" criterion to select the clusters. Need to initialize the number of clusters beforehand. BIB005 BIB002 BIB006 BIB007   BIB004 BIB008 BIB009 Partitioning Around Medoids (PAM) Partitioning Deal with interval-scaled measurements and general dissimilarity coefficients. Consumes large central memory size. [34]