Methods Of Optimization Clustering

Also, it is possible that the k-means algorithm won't find a final solution.  In this case it would be a good idea to consider stopping the algorithm after a pre-chosen maximum of

In this paper [45] proposed an approach that hybridization of partitioning K-Means algorithm and Fuzzy C-Means algorithm with Particle Swarm Optimization (PSO) gave better results than to all other existing algorithms. It was also noticed that as FCPSO deals nicely with the overlapping nature of document gives better results than KPSO.

Cluster analysis is the technique of grouping individuals into market segments on the basis of the multivariate survey information (Dolnicar, 2003). Market segmentation remains one of the most fundamental strategies for marketing. Organizations have to evaluate and choose the segments wisely as their target as this will determine how the organization will be in the marketplace. The quality of groupings management that an organization opts for is very paramount for the organizational success, and it calls for professional use of techniques to determine useful segments. Cluster analysis provides a plentiful of techniques employed in determining the number of segments and their characteristics (Wedel & Kamakura,

A swarm is a large number of homogenous, simple agents interacting locally among themselves, and their environment, with no central control to allow a global interesting behaviour to emerge. Swarm-based algorithms have recently emerged as a family of nature-inspired, population-based algorithms that are capable of producing low cost, fast, and robust solutions to several complex problems ‎ 1]‎ 2]. Swarm Intelligence [ [ (SI) can therefore be defined as a relatively new branch of Artificial Intelligence that is used to model the collective behaviour of social swarms in nature, such as ant colonies, honey bees, and bird flocks. Although these agents (insects or swarm individuals) are relatively unsophisticated with limited capabilities on their own, they are interacting together with certain behavioural patterns to cooperatively achieve tasks necessary for their survival. The social interactions among swarm individuals can be either direct or indirect ‎ 3]. Examples of direct interaction are through visual or audio contact, such as [ the waggle dance of honey bees. Indirect interaction occurs when one individual changes the environment and the other individuals respond to the new environment, such as the pheromone trails of ants that they deposit on their way to search for food sources. This indirect type of

Wide range of data is collected in different databases because of advanced techniques of data collection. The demand for grouping the valuable data and extracting only the useful information from data is increased. Clustering is the distribution of data into groups of identical objects which has similarity within the cluster and dissimilarity with the objects in the other groups [2]. Cluster analysis is the arrangement of a set of data into clusters of similar patterns [5]. Data within the same cluster are

The K-means algorithm is an unsupervised clustering algorithm which partitions a set of data, usually termed dataset into a certain number of clusters. Minimization of a performance index is the primary basis of K-means Algorithm, which is defined as the sum of the squared distances from all points in a cluster domain in the cluster center. Initially K random cluster centers are chosen. Then, each sample in the sample space is assigned to a cluster based on the minimum distance to the center of the cluster. Finally the cluster centers are updated to the average of the values in each cluster. This is repeated until cluster centers no longer change. Steps in the K-means algorithm are [K.M. Murugesan and S.

Abstract—In wireless sensor network (WSN), many novel architectures, protocols, algorithms and applications have been proposed and implemented for energy efficiency. The efficiency of these networks is highly dependent on routing protocols which directly affecting the network life-time. Cluster formation in sensor network is one of the most popular technique for reducing the energy consumption and expand the lifetime of the sensor network. There are various cluster formation techniques used in wireless sensor network. In which, Particle Swarm Optimization (PSO) is simple and efficient optimization algorithm, which is used to form the energy efficient clusters with optimal selection of cluster head. The comparison is made with the well-known cluster based protocols developed for WSN, LEACH (Low Energy Adaptive Clustering Hierarchy) and LEACH-C as well as the traditional K-means clustering algorithm. A comparative analysis shown in the paper and come to the conclusion based on some parameters.

The Bisecting K-means is based on K-means and can be run quickly for a big volume of data that have large dimensions. Thus it is appropriate for text clustering (Steinbach et al., 2000). At the beginning, all of the documents are in one partition. Then the following procedure will be repeated K 1 times to obtain K clusters. Initially the partition to be broken is selected. In the standard algorithm the partition with maximal cardinality is chosen, but in this research the one with maximal intra scatter has been selected as a low quality cluster. Intra

Chuang and Chien [2004] proposed to cluster and organize users’ queries into a hierarchical structure of topic classes. A Hierarchical Agglomerative Clustering (HAC) [25] algorithm is first employed to construct a binary-tree cluster hierarchy. The binary-tree hierarchy is then partitioned in order to create subhierarchies forming a multiway-tree cluster hierarchy like the hierarchical organization of Yahoo [6] and DMOZ [3].

Among the available clustering methods, K-Means algorithm is generally used to divide learners into natural groups based on their behavior for a larger dataset. In the K Means clustering method, the number of clusters, denoted by K is needed to be predefined to apply the technique. This is one of the simplest and the most used unsupervised learning algorithm for clustering.

Abstract— With the growth of GPS technology, the location based services in various technologies and devices supporting the location prediction and location based services has opened the area of research. The study and developing the efficiency of location based services has become a thrust area of research. Prediction of moving objects which can help to provide a better quality of service. The efficiency of the trajectory data depends on the efficiency and effective processing of the data. This paper involves the semantic methodology of the group movement of the people. The group based mining involves the process of identifying the group of people moving together and creating a weighted tree using the frequent pattern mining algorithm. The semantic representation will improve effectiveness in the process by efficient retrieval, indexing by annotation of the stay points.

2 Clustering is an important method for increase the scalability and lifetime of wireless sensor network system. The sensor network is divided into clusters.

In the case of Image Recognition the concept of clustering can be applied to identify the clusters in handwritten character recognition systems. Many applications of clustering are also found in Web search. Clustering can be utilized to organize the query results in groups and present the outcomes in a concise and effectively available way. We can distinguish and sparse regions in object space by automated clustering and from that we can find general interesting correlations and overall distribution patterns among data attributes. Cluster analysis has been broadly utilized as a part of various applications, like market research, pattern recognition, data analysis, and image processing. In business, clustering can offer marketers some assistance with discovering distinct groups in their client bases and portray client groups taking into account the purchasing patterns. In science, it can be utilized to determine plant and animal scientific categorizations, order qualities with similar functionality, and addition knowledge into structures inborn in populations. For the identification of the same land use in the earth observation database we use clustering. It can also be used for finding groups of houses in a city based on the house type, value and the geographic location. It will be helpful in identifying the groups of the policy holders with the highest average claim cost of the automobile insurance.

Provision of education for each & every student should be the basic initiative for the government in colleges & universities. For higher education many students are short of their tuition fees with popularization of their educational course. In customer segmentation (RFM) i.e. Recency, frequency & monetary method plays an important role. The prime goal in this case study is to build customer segmentation RFM model in a university for needy students through dining room database. After collecting the database this study can be applied using K-means algorithm to identify students. Through case study, the needy students list can be generated & can be provided to the department of university as a reference.

The final result is a tree like structure referred as Dendrogram, which shows the way the clusters are related. User can specify a distance or number of clusters to view the dataset in disjoint groups. In this way, the user can get rid of a cluster that does not serve any purpose as per his expertise. In this case, we used MVA (Multivariate data analysis) node in optimization package: modeFRONTIER (ESTECO, 2015) and other statistical software IBM SPSS (IBMSPSS, 2015) for HCA analysis.