In this paper a Pattern Recognition algorithm based on a constrained version of the k-means clustering algorithm will be presented. The proposed algorithm is a non parametric supervised statistical pattern recognition algorithm, i.e. it works under very mild assumptions on the dataset. The performance of the algorithm will be tested, togheter with a feature extraction technique that captures th...

Supervised learning tasks can require a large collection of labeled data for accurate pattern recognition. For recognition of handwritten characters, manually producing ground truths can be very tedious. In this paper, we propose a semisupervised hierarchical clustering method to reduce the necessary amount of human effort required for labeling a dataset of handwritten characters. The experimen...

Distance or similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance/similarity measures that are applicable to compare two probability density functions, pdf in short, are reviewed and categorized in both syntactic and semantic relationships. A correlation coefficient and a hierarchical clustering ...

This paper presents an emergent pattern recognition approach based on the immune network theory and hierarchical clustering algorithms. The immune network allows its components to change and learn patterns by changing the strength of connections between individual components. The presented immunenetwork-based approach achieves emergent pattern recognition by dynamically generating an internal i...

This paper proposes a novel method for detect,ing the optimal sequence of prosodic phrases from continuous speech based on data-driven approach. The pitch pattern of input speech is divided into prosodic segments which minimized the overall distortion with pitch pattern templates of accent phrases by using the One Pass search algorithm. The pitch pattern templates are designed by clustering a l...

Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe comp...

Cluster analysis is reformulated as a problem of estimating the para- meters of a mixture of multivariate distributions. The maximum-likelihood theory and numerical solution techniques are developed for a fairly general class of distributions. The theory is applied to mixtures of multivariate nor- mals (NORMIX) and mixtures of multivariate Bernoulli distributions (Latent Classes). The feasibili...

The concept of Support Vector Machines (SVMs) for classification and regression has been introduced by Vapnik in 1995. The classification algorithm proposed by Vapnik was intended as binary classification for linearly separable input data and the solution was to find an Optimal Separating Hyperplane (OSH). Very often the input data is not linearly separable. SVMs employ a technique commonly kno...

Pattern-based clustering has broad applications in microarray data analysis, customer segmentation, e-business data analysis, etc. However, pattern-based clustering often returns a large number of highlyoverlapping clusters, which makes it hard for users to identify interesting patterns from the mining results. Moreover, there lacks of a general model for pattern-based clustering. Different kin...