We present minimax bounds for classification and clustering error in the setting where covariates are drawn from a mixture of two isotropic Gaussian distributions. Here, we define clustering error in a discriminative fashion, demonstrating fundamental connections between classification (supervised) and clustering (unsupervised). For both classification and clustering, our lower bounds show that...

MOTIVATION The clustering of expressed sequence tags (ESTs) is a crucial step in many sequence analysis studies that require a high level of redundancy. Chimeric sequences, while uncommon, can make achieving the optimal EST clustering a challenge. Single-linkage algorithms are particularly vulnerable to the effects of chimeras. To avoid chimera-facilitated erroneous merges, researchers using si...

Gene microarray technology allows for unprecedented and massive production of biological data across multiple experimental conditions and in time series. Computer analysis of this data can help guide biological bench work toward the assignment of gene function, classification of cells and tissues and the ultimately assist in the diagnosis and treatment of disease. One approach to the analysis o...

Acknowledgements I had the great pleasure of working with Dr. Anamitra Makur during my stay at the Institute. But for him, this thesis would not have seen light. I would like to thank him for the help he has rendered during this work. Suryan for their help. Jyothish and Jemlin for making my stay at the Institute a happy and memorable one. P r a h h and Mala who had been always helpful to me. Fi...

Data clustering is typically considered a subjective process, which makes it problematic. For instance, how does one make statistical inferences based on clustering? The matter is di0erent with pattern classi1cation, for which two fundamental characteristics can be stated: (1) the error of a classi1er can be estimated using “test data,” and (2) a classi1er can be learned using “training data.” ...

In a strapdown magnetic compass, heading angle is estimated using the Earth's magnetic field measured by Three-Axis Magnetometers (TAM). However, due to several inevitable errors in the magnetic system, such as sensitivity errors, non-orthogonal and misalignment errors, hard iron and soft iron errors, measurement noises and local magnetic fields, there are large error between the magnetometers'...

Pairwise clustering methods partition the data space into clusters by the pairwise similarity between data points. The success of pairwise clustering largely depends on the pairwise similarity function defined over the data points, where kernel similarity is broadly used. In this paper, we present a novel pairwise clustering framework by bridging the gap between clustering and multi-class class...

BACKGROUND Recent reviews have shown that while clustering is extremely common in individually randomised trials (for example, clustering within centre, therapist, or surgeon), it is rarely accounted for in the trial analysis. Our aim is to develop a general framework for assessing whether potential sources of clustering must be accounted for in the trial analysis to obtain valid type I error r...

An ATM clustering system is a kind of workstation clusters over an ATM network. Such a system can be used as a distributed database server which requires reliable data delivery. This paper proposes an error recovery scheme at the transport layer for reliable data transfers with high throughput in the ATM clustering system. For such data transfers, acknowledgments are sent periodically as well a...

Combination of Adoptive Network based Fuzzy Inference System (ANFIS) and subtractive clustering (SC) has been used for estimation of deformation modulus (Em) and rock mass strength (UCSm) considering depth of measurement. To do this, learning of the ANFIS based subtractive clustering (ANFISBSC) was performed firstly on 125 measurements of 9 variables such as rock mass strength (UCSm), deformati...