In this paper we propose a new metric to replace the Euclidean norm in c-means clustering procedures. On the basis of the robust statistic and the in1uence function, we claim that the proposed new metric is more robust than the Euclidean norm. We then create two new clustering methods called the alternative hard c-means (AHCM) and alternative fuzzy c-means (AFCM) clustering algorithms. These al...

Methods for identifying meaningful growth patterns of longitudinal trial data with both nonignorable intermittent and drop-out missingness are rare. In this study, a combined approach with statistical and data mining techniques is utilized to address the nonignorable missing data issue in growth pattern recognition. First, a parallel mixture model is proposed to model the nonignorable missing i...

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...

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...

This study accounts for Korean /n/-epenthesis from a usage-based perspective, by describing the reduced productivity of epenthesis as an analogical change in progress. We found that epenthesis probability rises as whole-word frequency increases, supporting the hypothesis that analogical change begins in lowfrequency words (Bybee 2002). We interpret the findings as support for the idea that freq...

Malware Classification has been a challenging problem in the recent past and several researchers have attempted to solve this problem using various tools. It is security threat which can break machine operation while not knowing user’s data and it's tough to spot its behavior. This paper proposes a novel technique using DBSCAN (Density based Kmeans) algorithmic rule to spot the behavior of malw...

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...

Clustering is the process of partitioning a dataset into groups based on the similarity between the instances. Many clustering algorithms were proposed, but none of them proved to provide good quality partition in all situations. Consensus clustering aims to enhance the clustering process by combining different partitions obtained from different algorithms to yield a better quality consensus so...

Efficient Algorithms for Clustering and Classifying High Dimensional Text and Discretized Data using Interesting Patterns Hassan H. Malik Recent advances in data mining allow for exploiting patterns as the primary means for clustering and classifying large collections of data. In this thesis, we present three advances in pattern-based clustering technology, an advance in semi-supervised pattern...