Approximate nearest neighbor search (ANNS) constitutes an important operation in a
multitude of applications, including recommendation systems, information retrieval, and …

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

Information retrieval (IR) has changed considerably in recent years with the expansion of the
World Wide Web and the advent of modern and inexpensive graphical user interfaces and …

We propose a novel approach to solving the approximate k-nearest neighbor search
problem in metric spaces. The search structure is based on a navigable small world graph …

The ubiquity of time series data across almost all human endeavors has produced a great
interest in time series data mining in the last decade. While dozens of classification …

The problem of inducing, learning or inferring grammars has been studied for decades, but
only in recent years has grammatical inference emerged as an independent field with …

Existing studies on time series are based on two categories of distance functions. The first
category consists of the Lp-norms. They are metric distance functions but cannot support …

Outlier detection is an integral part of data mining and has attracted much attention recently
[M. Breunig et al.,(2000)],[W. ** et al.,(2001)],[E. Knorr et al.,(2000)]. We propose a new …

The area of similarity searching is a very hot topic for both research and c-mercial
applications. Current data processing applications use data with c-siderably less structure …

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