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is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and k- medoids. The different hierarchical techniques studied here are BIRCH and CHAMELEON. The different grid-based techniques which are described are DBSCAN and DENCLUE. Lastly, the different techniques which are used in grid-based technique, like STING and CLIQUE are described. This paper aims to provide a brief overview and comparison of these different clustering algorithms and methods.

Commerce recommender systems are affected by various kinds of profile-injection attacks where several fake user profiles are entered into the system to influence the recommendations made to the users. We have used Partition around Medoid (PAM) and Enhanced Clustering Large Applications Based on Randomized Search (ECLARANS) clustering algorithms of detecting such attacks by using outlier analysis. In user rating dataset, attack-profiles are considered as outliers in these algorithms. Firstly, we have used PAM and ECLARANS clustering algorithm in detecting the attack-profiles. These both algorithms have been applied for evaluating the performance of the system in identifying the attack profiles when they enter into the system. Experiments show that an accuracy of ECLARANS algorithm for detection of profile-injection attack for E-commerce recommender system is more than PAM clustering algorithm. KeywordsECLARANS, Outlier Analysis, Recommender System, profile-injection attack, Attack-profile.

This research paper proposed agent based framework for portfolio management using non-hierarchical clustering method. The framework included various agents such as data agent, clustering agent, ranking agent, portfolio manager and user agent. The data agent collected financial ratio of Nifty 50 companies from financial database. Clustering agents generated clusters and DB index computed to find optimum cluster size of each method. Validation agent evaluated the performance of k -means, k -medoids and fast k -means using intra-class inertia. Clusters generated by k -means used for investment and portfolio analysis using Markowitz model. This research helped to assemble a diversified portfolio of stocks with the use of clustering Keywords Clustering, Data mining (DM), Davis-Bouldin (DB) Index, Dunn Index, k -means, k -medoids, Partitioning Around Medoids(PAM), Silhouette index 1. INTRODUCTION Data mining is a process of automatically discovering knowledge and predicting future trends from large financial markets. It creates opportunities for companies to make proactive and knowledge-driven decision in order to gain a competitive advantage. There are varieties of DM techniques available over past decades that include classification, similarity search, cluster analysis, association rule mining. Data mining techniques are also widely applied in number of financial areas, including predicting stock prices, predicting stock indices, portfolio management, portfolio risk management, trend detection, designing recommender [27, 28]. Portfolio management is one of major problem in financial domain. In today‟s competitive financial environment, an investor wants to earn maximum profit from his assets. An investor considers an investment in securities faces with the problem of choosing from among a large number of securities. He confuses in which security he has to invest. It depends upon the risk-return characteristics of individual securities. He selects most desirable securities and likes to allocate his funds over this group of securities. Again, he faces with the problem of deciding which securities to select and how much to invest in each. The investor chooses the optimal portfolio taking into consideration the risk and return characteristics of all possible portfolios. The research work describes about an agent based framework for portfolio management using non-hierarchical clustering methods. The proposed framework consist of various agents such as data agent, clustering agent, ranking agent, user agent and portfolio manager. This framework assists investors in strategic planning and investment decision-making. This research work can help to assemble a diversified portfolio of stocks with the help of clustering and also will help investor community in specific and in turn it helps the society and economy in general for better allocation of wealth. In this research paper

is the procedure to group similar objects together. Several algorithms have been proposed for clustering. Among them, the K-means clustering method has less time complexity. But it is sensitive to extreme values and would cause less accurate clustering of the dataset. However, K- medoids method does not have such limitations. But this method uses user-defined value for K. Therefore, if the number of clusters is not chosen correctly, it will not provide the natural number of clusters and hence the accuracy will be minimized. In this paper, we propose a grid based clustering method that has higher accuracy than the existing K-medoids algorithm. Our proposed Grid Multi-dimensional K-medoids (GMK) algorithm uses the concept of cluster validity index and it is shown from the experimental results that the new proposed method has higher accuracy than the existing K- medoids method. The object space is quantized into a number of cells, and the distance between the intra cluster objects decrease which contributes to the higher accuracy of the proposed method. Therefore, the proposed approach has higher accuracy and provides natural clustering method which scales well for large dataset.