In this paper, I will test the Robust K-means algorithm, as developed and implemented by S. Still and L. Bottou, on randomly generated data sets taken from Gaussian distributions. I will then compare the results with the Soft K-means algorithm with Deterministic Annealing and the standard K-means algorithm, using several heuristics for initial centroid placement. I will perform experiments in b...

The k-means++ algorithm is the state of the art algorithm to solve k-Means clustering problems as the computed clusterings are O(log k) competitive in expectation. However, its seeding step requires k inherently sequential passes through the full data set making it hard to scale to massive data sets. The standard remedy is to use the k-means‖ algorithm which reduces the number of sequential rou...

This paper reflects the results of an implementation of the K-means algorithm on U.N survey data on people’s priorities, organized by country. The dataset includes 16 features for each country, with each feature corresponding to a different societal issue. Each country has a rating in the range of [0, 1] that indicates how important a particular feature or issue is to that country’s people– the...

In this study, two goals are followed. First, by means of the Generalized Differential Quadrature (GDQ) method, parametric analysis on the vibration characteristics of three-parameter Functionally Graded (FG) beams on variable elastic foundations is studied. These parameters include (a) three parameters of power-law distribution, (b) variable Winkler foundation modulus, (c) two-parameter elasti...

Correctly and effectively customer classification according to their characteristics and behaviors will be the most important resource for electronic marketing and online trading of network enterprises. Aiming at the shortages of the existing K-means algorithm of data-mining for customer classification, this paper advances a new customer classification algorithm through improving the existing K...

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized artificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dynamic local optimization strategy utili...

Beginning computer science students often have great difficulty with the analysis and design of solutions to programming related problems. Using the Shuttle Puzzle problem as a vehicle the process of analyzing a problem and synthesizing observations into an algorithm is presented in a detailed, step-by-step manner. Introduction When I was child and went to visit my grandparents, I would often e...

-In this paper targeted a variety of techniques, tactics and distinctive areas of the studies that are useful and marked because the crucial discipline of information mining technologies. The overall purpose of the system of statistics mining is to extract beneficial facts from a large set of information and changing it right into a shape that is comprehensible for in addition use. Clustering i...