This paper proposes a method to recognize the emotion present in the speech signal using Iterative clustering technique. We propose Mel Frequency Perceptual Linear Predictive Cepstrum (MFPLPC) as a feature for recognizing the emotions. This feature is extracted from the speech and the clustering models are generated for each emotion. For the Speaker Independent classification technique, preproc...

Protein sequence motifs are very important to the analysis of biologically significant conserved regions to determine the conformation, function and activities of the proteins. These sequence motifs are identified from protein sequence segments generated from large number of protein sequences. All generated sequence segments may not yield potential motif patterns. In this paper, short recurring...

IJSER © 2012 http://www.ijser.org Comparative Analysis of k-means and Enhanced K-means clustering algorithm for data mining Neha Aggarwal,Kirti Aggarwal, Kanika gupta ABSTRACT-K-Means Clustering is an immensely popular clustering algorithm for data mining which partitions data into different clusters on the basis of similarity between the data points and aims at maximizing the intra-class simi...

We consider the problem of retrieving objects from image data and learning to classify them into meaningful semantic categories with minimal supervision. To that end, we propose a fully differentiable unsupervised deep clustering approach to learn semantic classes in an end-to-end fashion without individual class labeling using only unlabeled object proposals. The key contributions of our work ...

To analyze topics of a large number of web events, we proposed an event topic analysis approach by topic feature clustering and extended LDA (latent dirichlet allocation) model. The extended LDA model is dimension LDA (DLDA) which integrates topic probability of LDA model. We represent an event as a multi-dimensions vector and use DLDA model to select topic feature words in events. We aggregate...

Kernel functions can be viewed as a non-linear transformation that increases the separability of the input data by mapping them to a new high dimensional space. The incorporation of kernel function enables the K-Means algorithm to explore the inherent data pattern in the new space. However, the recent applications of kernel KMeans algorithm are confined to small corpora due to its expensive com...

Data clustering is one of the most popular data mining techniques with broad applications. KMeans is one of the most popular clustering algorithms, due to its high efficiency/effectiveness and wide implementation in many commercial/non-commercial softwares. Performing efficient clustering on large dataset is especially useful; however, conducting K-Means clustering on large data suffers heavy c...

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...

Clustering is an essential task in Data Mining process which is used for the purpose to make groups or clusters of the given data set based on the similarity between them. K-Means clustering is a clustering method in which the given data set is divided into K number of clusters. This paper is intended to give the introduction about K-means clustering and its algorithm. The experimental result o...

The most popular algorithms used in unsupervised learning are clustering algorithms. Clustering to group samples into a number of classes or clusters based on the distances given sample features. Therefore, how define distance between is important for algorithm. Traditional generally Mahalanobis and Minkowski distance, which have difficulty dealing with set-based data uncertain nonlinear data. ...