The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity m...

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of eco...

The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity m...

A novel method based on shape morphing is proposed for 2D shape recognition. In this framework, the shape of objects is described by using their contour. Shape recognition involves a morph between the contours of the objects being compared. The morph is quanti"ed by using a physics-based formulation. This quanti"cation is used as a dissimilarity measure to "nd the reference shape most similar t...

The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity m...

In this paper, a similarity measure between genes with protein-protein interactions is proposed. The chip-chip data are converted into the same form of gene expression data with pearson correlation as its similarity measure. On the basis of the similarity measures of proteinprotein interaction data and chip-chip data, the combined dissimilarity measure is defined. The combined distance measure ...

K-Modes is an extension of K-Means clustering algorithm, developed to cluster the categorical data, where the mean is replaced by the mode. The similarity measure proposed by Huang is the simple matching or mismatching measure. Weight of attribute values contribute much in clustering; thus in this paper we propose a new weighted dissimilarity measure for K-Modes, based on the ratio of frequency...

Skeletal trees are commonly used in order to express geometric properties of the shape. Accordingly, tree edit distance is used to compute a dissimilarity between two given shapes. We present a new tree edit based shape matching method which uses a recent coarse skeleton representation. The coarse skeleton representation allows us to represent both shapes and shape categories in the form of dep...

Evolution acts in several ways on DNA : either by mutating a base, or inserting, deleting or copying a segment of the sequence 17, 18, ?]. Classical alignment methods deal with point mutations 19], genome-level mutations are studied using genome rearrangement distances 1, 2, 8, 9]. Those distances are mostly evaluated by a number of transpositions of genes. Here we deene a new distance, called ...

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...