Neha Bharill

A review of clustering techniques and developments

This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.

Enhanced cluster validity index for the evaluation of optimal number of clusters for Fuzzy C-Means algorithm

Cluster validity index is a measure to determine the optimal number of clusters denoted by (C) and an optimal fuzzy partition for clustering algorithms. In this paper, we proposed a new cluster validity index to determine an optimal number of hyper-ellipsoid or hyper-spherical shape clusters generated by Fuzzy C-Means (FCM) algorithm called as VIDSO index. The proposed validity index jointly exploits all the three measures named as intra-cluster compactness, an inter-cluster separation and overlap between the clusters. The proposed intra-cluster compactness is based on relative variability concept which is a statistical measure of relative dispersion or scattering of data in various dimensions within the clusters. The proposed inter-cluster separation measure indicates the isolation or distance between the fuzzy clusters. The proposed inter-cluster overlap measure determines the degree of overlap between the fuzzy clusters. The best fuzzy partition produced by the VIDSO index is expected to have low degree of intra-cluster compactness, higher degree of inter-cluster separation and low degree of inter-cluster overlap. The efficacy of VIDSO index is evaluated on six benchmark data sets and compared with a number of known validity indices. The experimental results and the comparative study demonstrate that, the proposed index is highly effective and reliable in estimating the optimal value of C and an optimal fuzzy partition for each data set because, it is insensitive with change in values of fuzzification parameter denoted by m. In contrast, the other indices [2], [3], [6], [7] fails to achieve the optimal value of C due to it is susceptibility with change in m.

Handling Big Data with Fuzzy Based Classification Approach

Big data is a collection of very large and complex data that is difficult to load into the computer memory. The major challenges include searching, categorization and analysis of big data. In this paper, a fuzzy based supervised classifier is proposed to handle the searching, storage and categorization of big data. In this classifier, we proposed a Random Sampling Iterative Optimization Fuzzy c-Means (RSIO-FCM) clustering algorithm which partitions the big data into various subsets. These subsets adequately cover all the instances (object space) of big data. Then, clustering is performed on these subsets by feeding forward the centers of clustered subset to group remaining subsets. Further, the designed classifier based on Bayesian theory is used to assign the labels to these clusters and also used to predict labels of unknown instances. Thus, the proposed approach results in effective clusters formation which also eliminates the problem of overlapping cluster centers faced by algorithm discussed in [1] named as Simple Random Sampling plus Extension FCM (rseFCM). The effectiveness of proposed clustering algorithm over rseFCM clustering is evaluated on two very large benchmark datasets in terms of fuzzification parameter m, objective function, computational time and accuracy. Experimental results demonstrate that, the RSIO-FCM algorithm generates more appropriate cluster centers location due to which it achieves better classification accuracy as compared to the rseFCM algorithm. Thus, it observed that, cluster centers location will have significant impact over classification results.

Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark

With the advancement in technology, a huge amount of data containing useful information, called Big Data, is generated on a daily basis. For processing such tremendous volume of data, there is a need of Big Data frameworks such as Hadoop MapReduce, Apache Spark etc. Among these, Apache Spark performs up to 100 times faster than conventional frameworks like Hadoop Mapreduce. For the effective analysis and interpretation of this data, scalable Machine Learning methods are required to overcome the space and time bottlenecks. Partitional clustering algorithms are widely adopted by researchers for clustering large datasets due to their low computational requirements. Thus, we focus on the design of partitional clustering algorithm and its implementation on Apache Spark. In this paper, we propose a partitional based clustering algorithm called Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) which is implemented on Apache Spark to handle the challenges associated with Big Data Clustering. Experimentation is performed on several big datasets to show the effectiveness of SRSIO-FCM in comparison with a proposed scalable version of the Literal Fuzzy c-Means (LFCM) called SLFCM implemented on Apache Spark. The comparative results are reported in terms of value of F-measure, ARI, Objective function, Run-time and Scalability. The reported results show the great potential of SRSIO-FCM for Big Data clustering.

Fuzzy Based Scalable Clustering Algorithms for Handling Big Data Using Apache Spark

A huge amount of digital data containing useful information, called Big Data, is generated everyday. To mine such useful information, clustering is widely used data analysis technique. A large number of Big Data analytics frameworks have been developed to scale the clustering algorithms for big data analysis. One such framework called Apache Spark works really well for iterative algorithms by supporting in-memory computations, scalability etc. We focus on the design and implementation of partitional based clustering algorithms on Apache Spark, which are suited for clustering large datasets due to their low computational requirements. In this paper, we propose Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) implemented on an Apache Spark Cluster to handle the challenges associated with big data clustering. Experimental studies on various big datasets have been conducted. The performance of SRSIO-FCM is judged in comparison with the proposed scalable version of the Literal Fuzzy c-Means (LFCM) and Random Sampling plus Extension Fuzzy c-Means (rseFCM) implemented on the Apache Spark cluster. The comparative results are reported in terms of time and space complexity, run time and measure of clustering quality, showing that SRSIO-FCM is able to run in much less time without compromising the clustering quality.

A Quantum-Inspired Fuzzy based Evolutionary algorithm for data clustering

In this paper, a Quantum-Inspired Evolutionary Fuzzy C-Means (QIE-FCM) algorithm is proposed. The proposed approach find the true number of clusters and the appropriate value of weighted exponent (m) which is required to be known in advance to perform clustering using Fuzzy C-Means (FCM) algorithm. However, the selection of inappropriate value of m and C may lead the algorithm to converge to the local optima. To address the issue of selecting the appropriate value of m and corresponding value of C. In QIE-FCM, the quantum concept is used in classical computer where m is represented in terms of quantum bits (qubits). The QIE-FCM is based on generations. At each generation (g), quantum gates are used to generate a new value of m. For each generated value of m, FCM algorithm is executed by varying values of C. Then, corresponding to m value appropriate value of C is identified by evaluating local fitness function for generation g. To achieve the global best value of m and C, the global fitness function is evaluated by comparing the local best fitness value in current generation with the best fitness value obtained among all the previous generations. To judge the efficacy of QIE-FCM algorithm, it is compared with two well-known indices and three evolutionary fuzzy based clustering algorithm and their performance is evaluated on four benchmark datasets. Furthermore, the sensitivity of QIE-FCM is also experimentally investigated in this paper.

An improved multiobjective simultaneous learning framework for designing a classifier

In this paper, an Improved Multiobjective Simultaneous learning framework for Designing a Classifier (IMSDC) is proposed. This learning algorithm is used to solve any multiclass classification problem. It is based on the framework proposed by Cai, Chen and Zhang [1] in 2010. In [1], multiple objective functions are utilized to formulate the problem of clustering and classification by employing Bayesian theory. In [1], the selection of learning parameter i.e., clusters membership degree uj (xi) is initially chosen at random, but here in the proposed methodology, the value of clusters membership degree uj (xi) is calculated on the basis of randomly initialized cluster centers. Experimental results show that, this method improve the performance by significantly reducing the number of iterations required to obtain the cluster center. The same is being verified with six benchmark datasets.

On Construction of Multi-class Binary Neural Network Using Fuzzy Inter-cluster Overlap for Face Recognition

In this paper, we propose a Novel Fuzzy-based Constructive Binary Neural Network (NF-CBNN) learning algorithm for multi-class classification. Our method draws a basic idea from Expand and Truncate Learning (ETL), which is a neural network learning algorithm. The proposed method works on the basis of unique core selection, and it guarantees to improve the classification performance by handling overlapping issues among data of various classes by using inter-cluster overlap. To demonstrate the efficacy of NF-CBNN, we tested it on the ORL face data set. The experimental results show that generalization accuracy achieved by NF-CBNN is much higher as compared to the BLTA classifier.

Quantum-inspired evolutionary approach for selection of optimal parameters of fuzzy clustering

Recently, Fuzzy c-Means (FCM) algorithm is most widely used because of its efficiency and simplicity. However, FCM is sensitive to the initialization of fuzziness factor (m) and the number of clusters (c) due to which it easily trapped in local optima. A selection of these parameters is a critical issue because an adverse selection can blur the clusters in the data. In the available fuzzy clustering literature, cluster validity index is used to determine the optimal number of clusters for the dataset, but these indexes may trap into the local optima due to the random selection of m. From the perspective of handling local optima problem, we proposed a hybrid fuzzy clustering approach referred as quantum-inspired evolutionary fuzzy c-means algorithm. In the proposed approach, we integrate the concept of quantum computing with FCM to evolve the parameter m in several generations. The evolution of fuzziness factor (m) with the quantum concept aims to provide the better characteristic of population diversity and large search space to find the global optimal value of m and its corresponding value of c. Experiments using three real-world datasets are reported and discussed. The results of the proposed approach are compared to those obtained from validity indexes like