An Enhanced Initialization Method to Find an Initial Center for K-modes Clustering

Data mining is a technique which extracts the information from the large amount of data. To group the objects having similar characteristics, clustering method is used. K-means clustering algorithm is very efficient for large data sets deals with numerical quantities however it not works well for real world data sets which contain categorical values for most of the attributes. K-modes algorithm is used in the place of K-means algorithm. In the existing system, the initialization of K- modes clustering from the view of outlier detection is considered. It avoids that various initial cluster centers come from the same cluster. To overcome the above said limitation, it uses Initial_Distance and Initial_Entropy algorithms which use a new weightage formula to calculate the degree of outlierness of each object. K-modes algorithm can guarantee that the chosen initial cluster centers are not outliers. To improve the performance further, a new modified distance metric -weighted matching distance is used to calculate the distance between two objects during the process of initialization. As well as, one of the data pre-processing methods is used to improve the quality of data. Experiments are carried out on several data sets from UCI repository and the results demonstrated the effectiveness of the initialization method in the proposed algorithm

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g., they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm could not identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, we developed an integer linear programming (ILP) approach for the k-modes clustering, which is independent to the initial solution and can obtain directly the optimal results for small-sized datasets. We also developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for near-optimal results of medium and large sized datasets with controlled computing time. Experiments on 38 datasets, including 27 synthesized small datasets and 11 known benchmark datasets from the UCI site were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experimental results outperformed the conventional and other existing enhanced k-modes algorithms in literature, updated 9 of the UCI benchmark datasets with new and improved results

Reorganization of Links to Improve User Navigation

Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure. For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used. Our aim is to perform reorganization with fewer changes in website structure

Approximating Spectral Clustering via Sampling: a Review

Spectral clustering refers to a family of unsupervised learning algorithms that compute a spectral embedding of the original data based on the eigenvectors of a similarity graph. This non-linear transformation of the data is both the key of these algorithms' success and their Achilles heel: forming a graph and computing its dominant eigenvectors can indeed be computationally prohibitive when dealing with more that a few tens of thousands of points. In this paper, we review the principal research efforts aiming to reduce this computational cost. We focus on methods that come with a theoretical control on the clustering performance and incorporate some form of sampling in their operation. Such methods abound in the machine learning, numerical linear algebra, and graph signal processing literature and, amongst others, include Nystr\"om-approximation, landmarks, coarsening, coresets, and compressive spectral clustering. We present the approximation guarantees available for each and discuss practical merits and limitations. Surprisingly, despite the breadth of the literature explored, we conclude that there is still a gap between theory and practice: the most scalable methods are only intuitively motivated or loosely controlled, whereas those that come with end-to-end guarantees rely on strong assumptions or enable a limited gain of computation time