In this paper we consider the problem of detecting commuting patterns in a trajectory. For this we search for similar subtrajectories. To measure spatial similarity we choose the Fréchet distance and the discrete Fréchet distance between subtrajectories, which are invariant under differences in speed. We give several approximation algorithms, and also show that the problem of finding the ‘longe...

This paper presents the usage of a general domain-independent data mining system in discovering of the Web user access patterns. DB2 Intelligent Miner for Data was successfully used in data mining of a huge Web log which was collected during the World FIFA Cup 1998. The clustering, associations and sequential pattern mining functions were considered in the context of Web usage mining. The clust...

Large knowledge bases, such as DBpedia, are most often created heuristically due to scalability issues. In the building process, both random as well as systematic errors may occur. In this paper, we focus on finding systematic errors, or anti-patterns, in DBpedia. We show that by aligning the DBpedia ontology to the foundational ontology DOLCEZero, and by combining reasoning and clustering of t...

In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementationally simple, and learn a di1erent set of feature weights for each identi2ed cluster. The cluster dependent feature weights o1er two advantages. First, they guide the clustering process to partition th...

ÐIn this paper, we propose a modified version of the K-means algorithm to cluster data. The proposed algorithm adopts a novel nonmetric distance measure based on the idea of apoint symmetry.o This kind of apoint symmetry distanceo can be applied in data clustering and human face detection. Several data sets are used to illustrate its effectiveness. Index TermsÐData clustering, pattern recogniti...

One of main aims of the spatial analysis of health and medical datasets is to provide additional information to the specialized medical research. These analyses can be used for disease mapping; searching for places with a higher intensity and probability of the disease event; or the influence assessment of selected natural or artificial phenomena. Suitably selected methods allow a proper analys...

In this paper we apply three pattern recognition methods (support vector machine, cluster analysis and principal component analysis) to distinguish regulatory regions from coding and non-coding non regulatory DNA sequences. Using a new feature representation (the degree by which motifs are overand under-represented) we demonstrate the remarkable power of this methodology in identifying regulato...

Dynamic pattern analysis refers to analyzing the relationship of spatial patterns at different time points. Traditional spatial pattern analysis such as data clustering can find the spatial patterns extant at a geographical location at a particular time point but failing to identify spatial dynamics, or changes that occur over time in a particular place. In this paper, we present a dynamic patt...

Document digitization and Document Analysis and Recognition (DAR) are techniques that are used for handling document images. Several techniques were implemented to perform document digitization. Article reconstruction is one of the main applications of document digitization. The four major steps of article reconstruction are grouping the article bodies, detecting the reading order, title body p...

Clustering or co-clustering techniques have been proved useful in many application domains. A weakness of these techniques remains the poor support for grouping characterization. As a result, interpreting clustering results and discovering knowledge from them can be quite hard. We consider potentially large Boolean data sets which record properties of objects and we assume the availability of a...