Clustering techniques have more importance in data mining especially when the data size is very large. It is widely used in the fields including pattern recognition system, machine learning algorithms, analysis of images, information retrieval and bio-informatics. Different clustering algorithms are available such as Expectation Maximization (EM), Cobweb, FarthestFirst, OPTICS, SimpleKMeans etc...

Web Usage Mining used to extract knowledge from WWW. Nowadays interaction of user towards web data is growing, web usage mining is significant in effective website management, adaptive website creation, support services, personalization, and network traffic flow analysis and user trend analysis and user’s profile also helps to promote website in ranking. Agglomerative clustering is a most flexi...

We propose a novel method for clustering allophones called Feature-Dependent Allophone Clustering (FD-AC) that determines feature-dependent HMM topology automatically. Existing methods for allophone clustering are based on parameter sharing between the allophone models that resemble each other in behaviors of feature vector sequences. However, all the features of the vector sequences may not ne...

Parameter clustering is a popular robust estimation technique based on location statistics in a parameter space where parameter samples are obtained from data samples. A problem with clustering methods is that they produce estimates not invariant to transformations of the parameter space. This article presents three contributions to the theoretical study of parameter clustering. First, it intro...

Two neural network models, called clustering-RBFNN and clustering-BPNN models, are created for estimating the work zone capacity in a freeway work zone as a function of seventeen different factors through judicious integration of the subtractive clustering approach with the radial basis function (RBF) and the backpropagation (BP) neural network models. The clustering-RBFNN model has the attract...

Clustering is essential to many tasks in pattern recognition and computer vision. With the advent of deep learning, there is an increasing interest in learning deep unsupervised representations for clustering analysis. Many works on this domain rely on variants of auto-encoders and use the encoder outputs as representations/features for clustering. In this paper, we show that an l2 normalizatio...

Relational clustering with heterogeneous data objects has impact in various important applications, such as web mining, text mining and bioinformatics etc. In this paper, we build a star-structured general model for relational clustering. It is formulated as an orthogonal tri-nonnegative matrix factorization. The model performs matrix approximation among all different data types to look for hid...

We address the problem of communicating domain knowledge from a user to the designer of a clustering algorithm. We propose a protocol in which the user provides a clustering of a relatively small random sample of a data set. The algorithm designer then uses that sample to come up with a data representation under which kmeans clustering results in a clustering (of the full data set) that is alig...

Data clustering consists of a group of procedures used to collect similar entries or data points within a set into clusters. No existing clustering echnique considers entries sequentially in time. In some cases, it is desirable to generate clusters that represent a segment of a time-ordered data et. For these purposes, an order-specific clustering algorithm is proposed. The proposed algorithm e...

We present a test of different error estimators for two-point clustering statistics, appropriate for present and future large galaxy redshift surveys. Using an ensemble of very large dark matter CDM N-body simulations, we compare internal error estimators (jackknife and bootstrap) to external ones (Monte Carlo realizations). For three-dimensional clustering statistics, we find that none of the ...