A Genetic Algorithm for K-Mean Clustering Varsha Singh Asst. Prof. JSSATE, Noida, Uttar Pradesh, India Prof A K Misra Professor, Deptt of CSE, MNNIT Allahabad, Uttar Pradesh, India _________________________________________________________________________________________ Abstract: Clustering techniques have obtained adequate results when are applied to data mining problems. Clustering is the pro...

Recommender systems apply data analysis techniques to the problem of helping users find the items they would like to purchase at E-Commerce sites by producing a predicted likeliness score or a list of top-N recommended items for a given user. We apply Clustering algorithms for finding nearest similar item. To finding nearest item for this we use C++ language. We apply improved K-mean algorithms...

The data stream model has recently attracted attention for its applicability to numerous types of data, including telephone records, Web documents, and click streams. For analysis of such data, the ability to process the data in a single pass, or a small number of passes, while using little memory, is crucial. D-Stream algorithm is an extended grid-based clustering algorithm for different dimen...

Clustering is associate automatic learning technique geared toward grouping a collection of objects into subsets or clusters. The goal is to form clusters that are coherent internally, however well completely different from one another. In plain words, objects within the same cluster ought to be as similar as potential, whereas objects in one cluster ought to be as dissimilar as potential from ...

Clustering is a method which divides data objects into groups based on the information found in data that describes the objects and relationships among them. There are a variety of algorithms have been developed in recent years for solving problems of data clustering. Data clustering algorithms can be either hierarchical or partitioned. Most promising among them are K-means algorithm which is p...

in hilbert space l2(rn), we prove the equivalence between the mod-ulus of smoothness and the k-functionals constructed by the sobolev space cor-responding to the fourier transform. for this purpose, using a spherical meanoperator.

This paper considers the problem of estimating a high-dimensional vector of parameters θ ∈ R from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension n exceeds two. The JS-estimator shrinks the observed vector towards th...

In this paper we study the average distance in weighted graphs. More precisely, we consider assignments of families of non-negative weights to the edges. The aim is to maximise (minimise, respectively) the average distance in the resulting weighted graph. Two variants of the problem are considered depending on whether the collection of weights is fixed or not. The main results of this paper are...