Discovery of Patterns and evaluation of Clustering Algorithms in SocialNetwork Data (Face book 100 Universities) through Data Mining Techniques and Methods

Abstract

Data mining involves the use of advanced data analysis tools to find out new, suitable patterns and project the relationship among the patterns which were not known prior. In data mining, association rule learning is a trendy and familiar method for ascertaining new relations between variables in large databases. One of the emerging research areas under Data mining is Social Networks. The objective of this paper focuses on the formulation of association rules using which decisions can be made for future Endeavour. This research applies Apriori Algorithm which is one of the classical algorithms for deriving association rules. The Algorithm is applied to Face book 100 university dataset which has originated from Adam D'Angelo of Face book. It contains self-defined characteristics of a person including variables like residence, year, and major, second major, gender, school. This paper to begin with the research uses only ten Universities and highlights the formation of association rules between the attributes or variables and explores the association rule between a course and gender, and discovers the influence of gender in studying a course. This paper attempts to cover the main algorithms used for clustering, with a brief and simple description of each.The previous research with this dataset has applied only regression models and this is the first time to apply association rules.

Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis

“…Clustering [41] is an unsupervised data mining technique that groups data based on a similarity measure. The blood vessel segmentation problem constitutes only two groups namely 'vessel' and 'non-vessel'.…”

Automatic concept clustering for ontological structure through data mining techniques

“…Expectation Maximization (EM) [18] algorithm is used to classify each point into the most likely Gaussian and estimate the parameters of each distribution. The algorithm is shown in Fig 2. Set initial partition r ei randomly.…”

“…The algorithm is shown in Fig 2. Set initial partition r ei randomly. REPEAT Set the weight parameter w ei CALL the mining algorithm to obtain F Estimate θ ek and θ ok only for k € F (M-step) Update the posterior r ei (E-step) UNTIL the convergence K-Means clustering [18] finds the cluster centers and assigns the objects to the nearest cluster center, such that the squared distances from the cluster are minimized. It is an optimization problem.…”

“…Farthest First [18] is a variant of K-Means that differs in the initial centroid assignment. It places each cluster centre in turn at the point furthest from the existing cluster centres.…”

“…For the current research work, since the classes are not known earlier, unsupervised clustering techniques are sought for. Clustering techniques [18] viz. EM, Farthest First, K-Means were implemented and attempted for grouping of the concepts for the zoo ontology dataset obtained from UCI Machine Learning Repository [19].…”