Harmony Search-Based Cluster Initialization For Fuzzy C-Means Segmentation Of MR Images.

We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem

Feature Selection Inspired Classifier Ensemble Reduction

Classifier ensembles constitute one of the main research directions in machine learning and data mining. The use of multiple classifiers generally allows better predictive performance than that achievable with a single model. Several approaches exist in the literature that provide means to construct and aggregate such ensembles. However, these ensemble systems contain redundant members that, if removed, may further increase group diversity and produce better results. Smaller ensembles also relax the memory and storage requirements, reducing system's run-time overhead while improving overall efficiency. This paper extends the ideas developed for feature selection problems to support classifier ensemble reduction, by transforming ensemble predictions into training samples, and treating classifiers as features. Also, the global heuristic harmony search is used to select a reduced subset of such artificial features, while attempting to maximize the feature subset evaluation. The resulting technique is systematically evaluated using high dimensional and large sized benchmark datasets, showing a superior classification performance against both original, unreduced ensembles, and randomly formed subsets. ? 2013 IEEE

Hybrid Clustering with Application to Web Pages

This project explains the process of clustering web pages. With the immense increase in the number of web pages available on the internet, it has become difficult to search for web pages. The clustering of web pages will improve the presentation of web pages to the user and saves the time spent on searching web pages. Various clustering techniques have been proposed by various research scientists to cluster the web pages, but all the techniques suggested have some drawbacks. Since there is lot of scope for further improvement in the field of clustering, the system proposed in this report takes the clustering of web pages a step ahead. The proposed system use the queries from the user and get the results from search engine, then processes the results and provides the final result clusters to users