Clustering is crucial to many applications in pattern recognition, data mining, and machine learning. Evolutionary techniques have been used with success in clustering, but most suffer from several shortcomings. We formulate requirements for efficient encoding, resistance to noise, and ability to discover the number of clusters automatically.

In this paper we make two novel contributions to hierarchical clustering. First, we introduce an anomalous pattern initialisation method for hierarchical clustering algorithms, called A-Ward, capable of substantially reducing the time they take to converge. This method generates an initial partition with a sufficiently large number of clusters. This allows the cluster merging process to start f...

Clustering is one of the most important task in pattern recognition. For most of partitional clustering algorithms, a partition that represents as much as possible the structure of the data is generated. In this paper, we adress the problem of finding the optimal number of clusters from data. This can be done by introducing an index which evaluates the validity of the generated fuzzy c-partitio...

Nowadays melanoma is one of the most important cancers to study due to its social impact. This dermatologic cancer has increased its frequency and mortality during last years. In particular, mortality is around twenty percent in non early detected ones. For this reason, the aim of medical researchers is to improve the early diagnosis through a best melanoma characterization using pattern matchi...

Flow cytometry is a technique that is used to count cells and to characterize property of the cells. In spite of enormous information content on the cells provided by flow cytometry, cytometry data is still analyzed based on stepby-step gating, either manually or automatically via bioinformatics. This paper presents a new strategy of interpreting cytometry data in a different manner. The propos...

We present a large-scale analysis of activity on Twitter in 50 major cities around the world throughout all of 2012. Our study consists of two parts: First, we created heatmap visualizations, through which periods of comparatively intense and sparse activity are readily apparent—these visual patterns reflect diurnal cycles, cultural norms, and even religious practices. Second, we performed a cl...

Our paper reports an attempt to apply an unsupervised clustering algorithm to a Hungarian treebank in order to obtain semantic verb classes. Starting from the hypothesis that semantic metapredicates underlie verbs’ syntactic realization, we investigate how one can obtain semantically motivated verb classes by automatic means. The 150 most frequent Hungarian verbs were clustered on the basis of ...

This paper presents an analysis of different learning styles observed in a group of college freshmen. Recognizing relevant aspects of each style provides aid in the planning of actions that could reduce dropouts and increase the academic performance of first-year students in colleges. To accomplish this study an assessment tool was devised and implemented applying techniques of clustering and m...

This thesis introduces a new partitioning algorithm to cluster variables in high dimensional low sample size (HDLSS) data and high dimensional longitudinal low sample size (HDLLSS) data. HDLSS data contain a large number of variables with small number of replications per variable, and HDLLSS data refer to HDLSS data observed over time. Clustering technique plays an important role in analyzing h...

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual...