A cluster operator takes a set of data points and partitions the points into clusters (subsets). As with any scientific model, the scientific content of a cluster operator lies in its ability to predict results. This ability is measured by its error rate relative to cluster formation. To estimate the error of a cluster operator, a sample of point sets is generated, the algorithm is applied to e...

This paper deals with the local learning approach for clustering, which is based on the idea that in a good clustering, the cluster label of each data point can be well predicted based on its neighbors and their cluster labels. We propose a novel local learning based clustering algorithm using kernel regression as the local label predictor. Although sum of absolute error is used instead of sum ...

This paper analyses a multi-compressor system for its performance failures and subsequent improvements. The logbook data of this system has been obtained. Data has been classified using various state-of-art data classification techniques. This paper presents a comparative analysis of Fuzzy clustering algorithm, Hard-c-means clustering and Gustafson-Kessel clustering algorithm. Data clustering e...

Histograms are the workhorse of data mining and analysis. This paper considers the problem of publishing histograms under differential privacy, one of the strongest privacy models. Existing differentially private histogram publication schemes have shown that clustering (or grouping) is a promising idea to improve the accuracy of sanitized histograms. However, none of them fully exploits the ben...

In this paper we develop a Potts spin neural clustering method for clustering belief functions based on attracting and conflicting metalevel evidence. Such clustering is useful when the belief functions concern multiple events, and all belief functions are mixed up. The clustering process is used as the means for separating the belief functions into clusters that should be handled independently...

This paper proposes Centroid Neural Network with Directional Spawning for efficient data clustering. The proposed algorithm locates each new neuron for cluster center by considering the location trajectory of previously added neurons during training process. Experiments on different data sets are performed in order to evaluate the performance of training error, training speed, and test error. T...

This paper presents a multi-stage algorithm for multichannel ECG beat classification into normal and abnormal categories using a sequential beat clustering and a crossdistance analysis algorithm. After clustering stage, a search algorithm is applied to detect the main normal class. Then other clusters are classified based on their distance from the main normal class. The algorithm is developed ...

We present a new clustering algorithm for global illumination in complex environments. The new algorithm extends previous work on clustering for radiosity to allow for nondiffuse (glossy) reflectors. We represent clusters as points with directional distributions of outgoing and incoming radiance and importance, and we derive an error bound for transfers between these clusters. The algorithm gro...

Spectral clustering is useful for a wide-ranging set of applications in areas such as biological data analysis, image processing and data mining. However, the computational and/or communication resources required by the method in processing large-scale data are often prohibitively high, and practitioners are often required to perturb the original data in various ways (quantization, downsampling...