Knowledge discovery in databases presents many interesting challenges within the ¢onte~t of providing computer tools for exploring large data archives. Electronic data .repositories are growing qulckiy and contain data from commercial, scientific, and other domains. Much of this data is inherently temporal, such as stock prices or NASA telemetry data. Detect£ug patterns in such data streams or ...

A holistic approach, in the form of a process, is proposed in order to discover marketing intelligence from Internet data. The Internet-enabled knowledge discovery process contains the steps human resource identification, problem specification, data prospecting, domain knowledge elicitation, methodology identification, data pre-processing, pattern discovery, and knowledge post-processing. It al...

The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an ...

This paper describes applications of the optimized pattern discovery framework to text and Web mining. In particular, we introduce a class of simple combinatorial patterns over phrases, called proximity phrase association patterns, and consider the problem of finding the patterns that optimize a given statistical measure within the whole class of patterns in a large collection of unstructured t...

Recently, inductive databases (IDBs) have been proposed to tackle the problem of knowledge discovery from huge databases. With an IDB, the user/analyst performs a set of very different operations on data using a query language, powerful enough to support all the required manipulations, such as data preprocessing, pattern discovery and pattern post-processing. We provide a comparison between thr...

Pattern discovery in unaligned DNA sequences is a fundamental problem in computational biology with important applications in finding regulatory signals. Current approaches to pattern discovery focus on monad patterns that correspond to relatively short contiguous strings. However, many of the actual regulatory signals are composite patterns that are groups of monad patterns that occur near eac...

Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operations on data using a special-purpose language, powerful enough to perform all the required manipulations, such as data preprocessing, pattern discovery and pattern post-processing. In this paper we present a ...

Frequent closed pattern discovery is one of the most important topics in the studies of the compact representation for data mining. In this paper, we consider the frequent closed pattern discovery problem for a class of structured data, called attribute trees (AT), which is a subclass of labeled ordered trees and can be also regarded as a fragment of description logic with functional roles only...

Consecutive pattern mining aiming at finding sequential patterns substrings, is a special case of frequent pattern mining and has been played a crucial role in many real world applications, especially in biological sequence analysis, time series analysis, and network log mining. Approximations, including insertions, deletions, and substitutions, between strings are widely used in biological seq...