This article proposes a constrained clustering algorithmwith competitive performance and less computation time to the state-of-the-art methods, which consists of a constrained k-means algorithm enhanced by the boosting principle. Constrained k-means clustering using constraints as background knowledge, although easy to implement and quick, has insufficient performance compared with metric learn...

The k-means clustering algorithm, a staple of data mining and unsupervised learning, is popular because it is simple to implement, fast, easily parallelized, and offers intuitive results. Lloyd’s algorithm is the standard batch, hill-climbing approach for minimizing the k-means optimization criterion. It spends a vast majority of its time computing distances between each of the k cluster center...

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular clustering approach. However, after a bad initialization, poor local optima can be easily obtained. To tackle the initialization problem of k-Means, we propose the MinMax k-Means algorithm, a method that assigns weights to the clusters relative to their variance and optimizes a weighted version of the k-Mean...

The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial positions sometimes are bad, afte...

The K-means clustering algorithm is an old algorithm that has been intensely researched owing to its simplicity of implementation. However, there have also been criticisms on its performance, in particular, for demanding the value of K a priori. It is evident from previous researches that providing the number of clusters a priori does not in any way assist in the production of good quality clus...

K-means clustering is a popular clustering algorithm based on the partition of data. However, K-means clustering algorithm suffers from some shortcomings, such as its requiring a user to give out the number of clusters at first, and its sensitiveness to initial conditions, and its being easily trapped into a local solution et cetera. The global Kmeans algorithm proposed by Likas et al is an inc...

In this paper, we present a novel algorithm for performing k-means clustering. It organizes all the patterns in a k-d tree structure such that one can find all the patterns which are closest to a given prototype efficiently. The main intuition behind our approach is as follows. All the prototypes are potential candidates for the closest prototype at the root level. However, for the children of ...

K-means is an effective clustering technique used to separate similar data into groups based on initial centroids of clusters. In this paper, Normalization based K-means clustering algorithm(N-K means) is proposed. Proposed N-K means clustering algorithm applies normalization prior to clustering on the available data as well as the proposed approach calculates initial centroids based on weights...

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. About 10% of...

A cluster is a collection of data objects that are similar to each other and dissimilar to the data objects in other clusters. K-means algorithm has been used in many clustering work because of the ease of the algorithm. But time complexity of algorithm remains expensive when it applied on large datasets. To improve the time complexity, we implemented parallel k-means algorithm for cluster larg...