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

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.

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

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.

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].

In this section three clustering algorithms which are used in this paper as baseline are briefly described. They are: Bisecting K-means, LSI and PLSI based clustering.

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.

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.

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,

Abstract—The main aim is to provide a comparison of different clustering algorithm techniques in data mining. Clustering techniques is broadly used in many applications such as pattern recognition, market research, image processing and data analysis. Cluster Analysis is an excellent data mining tool for a large and multivariate database. A cluster of data objects can be treated as one group. In clustering analysis our object is first partition the set of data into similar data groups and then assigns labels to those groups. Clustering is a suitable example of unsupervised classification.

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

[33] proposed clustering algorithm in which they integrate an improved discrete PSO into a divisive clustering framework. It was claim that it is very stable and also runs much faster than other clustering algorithms.

Segmentation is nothing but the portioning [9] of an image. Thus, the segmentation are done for the region of interest. There are so many segmentation method but level set segmentation reduces the problem for finding the curve that encloses the regions of interest.

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.

When all the items of the data set are assigned to one of the centroid the first stage is completed and an early set of clusters is obtained. After the first stage we recalculate to find the new centroids and then again find the distances between the data set entities and the centroids. The same process is iterated till the centroids become stable and there are no more changes in it. The K-Means algorithm is fast robust and easier to understand compared to the other clustering algorithms. Also it provides better results when the data items are well separated or distinct from each other.