Abstract
The paper focus on combination of K-Means algorithm for Fuzzy Mean Point Clustering Neural Network (FMPCNN). The algorithm is implemented in JAVA program code for implementing the movecentroid function code into FMPCNN. Here we have provided movecentroid’s output to Fuzzy clustering as criteria, movecentroid is the base function of K-means algorithm as in Fuzzy Mean Point Clustering Neural Network (FMPCNN) algorithm, calculation of cluster based on pre-defined criteria and scope is done. In the experiment we have used four datasets and observed results in nano seconds there is huge difference in output as time is reduced for Fuzzy Min-Max code execution of fuzzy calculations of clustering.
Highlights
Web Mining is an important sub-branch of Data Mining
Those patterns are grouped into a cluster, which gives fuzzy membership greater than or equal to bunching factor, for that cluster. This algorithm uses a cluster fuzzy set that is defined by a mean point(centroid), a minimum distance between pattern and mean point and a fuzzy membership function
For the given set of α1 and β, the learning algorithm finds optimal mean points of pattern clusters. It uses fuzzy mean point cluster membership function mj, of an input pattern Rh, in the jth cluster Cj, and δ i.e. the distance defining the core part of the cluster fuzzy set, is given as where f() is three-parameter ramp threshold function defined as get the membership parameter for items; assign criteria1 and criteria2 with movecentroid() function‟s output; criteria1 = K-Means_Centroid1
Summary
Web Mining is an important sub-branch of Data Mining. Data mining is extracting potential, unknown, useful information, patterns and trends from abundant, incomplete, noise and random data. The Web includes huge amount of data, using the data mining technology on the Web, namely the Web mining technology, becomes the most important research along with the rapid development of Internet [2]. Data mining has various techniques to extract useful information in large amounts of data. Data mining is defined as a technique of finding hidden information in a database [1]. It may be called as data driven discovery, explorative data analysis, deductive learning. Data mining in general falls in to the following categories: classification patterns, clustering, association patterns [2, 3]
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