Over the past few decades, the volume of existing text data increased exponentially. Automatic tools to organize these huge collections of documents are becoming unprecedentedly important. Document clustering is important for organizing automatically documents into clusters. Most of the clustering algorithms process document collections as a whole; however, it is important to process these docu...

Sampling and variational inference techniques are two standard methods for inference in probabilistic models, but for many problems, neither approach scales effectively to large-scale data. An alternative is to relax the probabilistic model into a non-probabilistic formulation which has a scalable associated algorithm. This can often be fulfilled by performing small-variance asymptotics, i.e., ...

The graded possibilistic clustering paradigm includes as the two extreme cases the “probabilistic” assumption and the “possibilistic” assumption adopted by many clustering algorithms. We propose an implementation of a graded possibilistic clustering algorithm based on an interval equality constraint enforcing both the normality condition and the required graded possibilistic condition. Experime...

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'...

In recent years, information-theoretic clustering algorithms have been proposed which assign data points to clusters so as to maximize the mutual information between cluster labels and data [1, 2]. Using mutual information for clustering has several attractive properties: it is flexible enough to fit complex patterns in the data, and allows for a principled approach to clustering without assumi...

The dramatic growth in the number of application domains that naturally generate probabilistic, uncertain data has resulted in a need for efficiently supporting complex querying and decision-making over such data. In this paper, we address the problem of on-the-fly clustering and ranking over probabilistic databases. We begin with a systematic exploration of ranking in probabilistic databases b...

We provide a probabilistic analysis of the output of Quicksort when comparisons can err.

Infinite groups have been used for cryptography since about twenty years ago. However, it has not been so fruitful as using finite groups. An important reason seems the lack of research on building a solid mathematical foundation for the use of infinite groups in cryptography. As a first step for this line of research, this paper pays attention to a property, the so-called right-invariance, whi...

handwritten digit recognition can be categorized as a classification problem. probabilistic neural network (pnn) is one of the most effective and useful classifiers, which works based on bayesian rule. in this paper, in order to recognize persian (farsi) handwritten digit recognition, a combination of intelligent clustering method and pnn has been utilized. hoda database, which includes 80000 p...