Essay On The Growth Of The Internet

Grouping similar customers and products has been used prominently in market segmentation and this is also the fundamental in marketing activity (E.Mooi and M.Sarstedt, 2011). This method is known as the cluster analysis and it is a multivariate method which classifies a sample

Big data analytics methods and techniques (which is the application of predictive methods, pattern recognition techniques, cluster analysis, and other quantitative and qualitative methods in big data sets) can

Abstract: Data clustering is the most important technique in studying data analysis and it is also important in researching several domains regarding the analysis for which sampling has been important to improve the efficiency of clustering. However, after the sampling applied those points that are not selected in sample have their labels after the normal process and even we have so many straight forward approach in numeric domain we have the problem of allocated these unlabeled

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.

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—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.

As one of the most popular and widely used data mining techniques, cluster analysis is mainly divided into hierarchical clustering and partitional clustering, which are carried out in a supervised or unsupervised way to separate data into different groups based on similar characteristics. Both the hierarchical and partitional clustering have advantages and drawbacks. Especially, the efficiency and accuracy are the primary challenges that clustering analysis has to face. For example, the most efficient algorithm of hierarchical clustering is complete-linkage clustering in some special cases, the complexity of which is Ο (n2). Therefore, the hierarchical clustering usually leads to too slow efficiency for large data

Clustering plays an important role in data mining process. Clustering is the approach of grouping the data into classes or clusters so that the objects within each cluster have high similarity in comparison with one another[12].The common approach of clustering techniques is that to find cluster centroid and then the data are clustered. Several clustering techniques are partitioning methods, hierarchical methods, density based methods, grid based methods, model based methods and constraint based clustering. Clustering is a challenging field of research in which its potential applications pose their own requirements [4]. Clustering is also called as the data segmentation because clustering method partitions the large data sets into smaller data groups according to their similarities. The main objective of cluster analysis is to increase intra-group similarity and inter-group dissimilarity.

In this section, we present an overview of web document clustering algorithms in some detail.

This technique is based on the popular k-means clustering algorithm. The clustering algorithm can work on data with many dimensions and target to reduce the distance within clusters. In the same time, it increases the distance between clusters. Initiating with K centers, the method iteratively assign each point to its nearest center dependent on

Due to the huge growth and expansion of the World Wide Web, a large amount of information is available online. Through Search engines we can easily access this information with the help of Search engine indexing. To facilitate fast and accurate information retrieval search engine indexing collects, parses, and store data. This paper explains partitioning clustering technique for implementing indexing phase of search engine. Clustering techniques are widely used for grouping a set of objects in such a way that objects in the same group are more to each other than to those in other groups in “Web Usage Mining”. Clustering methods are largely divided into two groups: hierarchical and partitioning methods. This paper proposes the k-mean partitioning method of clustering and also provide a comparison of k-mean clustering and Single link HAC . Performance of these clustering techniques are compared according to the execution time based on no of clusters and no of data items being entered.

In clustering and classification phase dissimilar data is differentiated from similar data. In our project this step can be omitted because the dataset is already in the form of a CSV file, divided according to the respective link types.

Document clustering is way of automatic organization of documents into clusters so that documents within a cluster have high similarity in comparison to documents in other clusters. It is measuring similarity between documents and grouping similar documents together. The study of similarity measure for clustering is initially motivated by a research on automated text categorization. There was several similarity measures used for document similarity. It provides client representation and visualization of the documents; thus helps in easy navigation also. It has been used intensively because of its wide applicability in various areas such as web mining, search engines, and information retrieval. The key of organizing data in such a way is to improve data availability and to fasten data access, so that web information retrieval and content delivery on the web are improved. The main idea is to improve the accessibility and usability of text mining for various applications. By optimizing similarity measures the optimal clusters can be formed thus performance is improved.

This paper show that if the data satisfies a natural condition on separation/clusterability, then a near-optimal clustering result can be expected as various Lloyd-style methods would have great performance in such setting.

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.