This paper proposes and implements a new solution for generating progressive vector lines and polygons data streaming for adaptive visualizations on small mobile devices. The proposed solution firstly recognizes the spatial patterns of vector data via a pattern recognition method, then generates a coarser Level_of_Detail of vector data with the preservation of main characteristics of vector dat...

Discovering patterns from data is an important task in data mining. There exist techniques to find large collections of many kinds of patterns from data very efficiently. A collection of patterns can be regarded as a summary of the data. A major difficulty with patterns is that pattern collections summarizing the data well are often very large. In this dissertation we describe methods for summa...

Crime forecasting is notoriously difficult. A crime incident is a multi-dimensional complex phenomenon that is closely associated with temporal, spatial, societal, and ecological factors. In an attempt to utilize all these factors in crime pattern formulation, we propose a new feature construction and feature selection framework for crime forecasting. A new concept of multi-dimensional feature ...

In the real world problems various pattern recognition technologies process huge amount of pattern to discover relevant knowledge. These techniques are computationally expensive. Additional knowledge also known as domain or background knowledge can help us in reducing the search as well as to optimize the hypotheses by decreasing the size of the search area. In the present paper we discuss the ...

A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the...

A fundamental activity common to image processing, pattern recognition, and clustering algorithm involves searching set of n , k-dimensional data for one which is nearest to a given target data with respect to distance function . Our goal is to find search algorithms with are full search equivalent -which is resulting match as a good as we could obtain if we were to search the set exhausting. 1...

In practice we are often faced with random experiments whose outcomes are not numbers (or vectors in &I”) but are expressed in inexact linguistic terms. As an example, consider a group of individuals chosen at random who are questioned about the weather on a particular city on a particular winter day. Some possible answers would be “cold,” “more or less cold,” “very cold,” “ extremely cold,” an...

We have focused on a set of problems related to image pattern recognition, presenting an approach for the graphical objects' detection problem. First, in the introduction, we describe the general aspects of the computer vision system. Then, we provide approaches for image's objects identification, by composing the previously detected textured and non--textured regions, for the shape classificat...

Microarrays generate a large volume of experimental data and many methods, such as clustering and classification, can be used to analyze this data. Initial investigation through exploratory analysis provides great insight into the data by detecting structures and patterns. After patterns have been identified, they can be used for both discovery and prediction of genes through various methods. A...