Cluster Validation Techniques Research Articles

Innovation in the cluster validating techniques

To detect database records containing approximate and exact duplicates because of data entry error or differences in the detailed schemas of records from multiple databases or for some other reasons is an important line of research. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of Silhouette width, Calinski & Harbasz index (pseudo F-statistics) and Baker & Hubert index (? index) algorithms for exact and approximate duplicates. In this paper, a comparative study and effectiveness of these three cluster validation techniques which involve measuring the stability of a partition in a data set in the presence of noise, in particular, approximate and exact duplicates are presented. Silhouette width, Calinski & Harbasz index and Baker & Hubert index are calculated before and after inserting the exact and approximate duplicates (deliberately) in the data set. Comprehensive experiments on glass, wine, iris and ruspini database confirms that the Baker & Hubert index is not stable in the presence of approximate duplicates. Moreover, Silhouette width, Calinski and Harbasz index and Baker & Hubert indice do not exceed the original data indice in the presence of approximate duplicates.

고차원 (유전자 발현) 자료에 대한 군집 타당성분석 기법의 성능 비교

유전자 발현 자료(gene expression data)는 전형적인 고차원 자료이며, 이를 분석하기 위한 여러 가지 군집 알고리즘(clustering algorithm)과 군집 결과들을 검증하는 군집타당성분석 기법(cluster validation technique)이 제안되고 있지만, 이들 군집 타당성을 분석하는 기법의 성능에 대한 비교, 평가는 매우 드물다. 본 논문에서는 저차원의 모의실험 자료와 실제 유전자 발현 자료에 대하여 군집 타당성분석 기법들의 성능을 비교하였으며, 그 결과 내적 측도에서는 Dunn 지수, Silhouette 지수 순으로 뛰어났고 외적 측도에서는 Jaccard 지수가 성능이 가장 우수한 것으로 평가되었다. Many clustering algorithms and cluster validation techniques for high-dimensional gene expression data have been suggested. The evaluations of these cluster validation techniques have, however, seldom been implemented. In this paper we compared various cluster validity indices for low-dimensional simulation data and real gene expression data, and found that Dunn's index is the most effective and robust, Silhouette index is next and Davies-Bouldin index is the bottom among the internal measures. Jaccard index is much more effective than Goodman-Kruskal index and adjusted Rand index among the external measures.

SeqPAM

With the growth in the number of Web users and necessity for making information available on the Web, the problem of Web personalization has become very critical and popular. Developers are trying to customize a Web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this chapter, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for Web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

Computational cluster validation in post-genomic data analysis

The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/. Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

Evaluating distance functions for clustering tandem repeats.

Tandem repeats are an important class of DNA repeats and much research has focused on their efficient identification, their use in DNA typing and fingerprinting, and their causative role in trinucleotide repeat diseases such as Huntington Disease, myotonic dystrophy, and Fragile-X mental retardation. We are interested in clustering tandem repeats into groups or families based on sequence similarity so that their biological importance may be further explored. To cluster tandem repeats we need a notion of pairwise distance which we obtain by alignment. In this paper we evaluate five distance functions used to produce those alignments--Consensus, Euclidean, Jensen-Shannon Divergence, Entropy-Surface, and Entropy-weighted. It is important to analyze and compare these functions because the choice of distance metric forms the core of any clustering algorithm. We employ a novel method to compare alignments and thereby compare the distance functions themselves. We rank the distance functions based on the cluster validation techniques--Average Cluster Density and Average Silhouette Width. Finally, we propose a multi-phase clustering method which produces good-quality clusters. In this study, we analyze clusters of tandem repeats from five sequences: Human Chromosomes 3, 5, 10 and X and C. elegans Chromosome III.

High-throughput powder diffraction. IV. Cluster validation using silhouettes and fuzzy clustering

In two previous papers [Gilmore, Barr & Paisley (2004).J. Appl. Cryst.37, 231–242; Barr, Dong & Gilmore (2004).J. Appl. Cryst.37, 243–252], it was demonstrated how to generate a correlation matrix by comparing full powder diffraction patterns, and then partition the diffractograms into groups using multivariate statistics and associated classification procedures. For clustering the patterns into related sets, dendrograms, metric multidimensional scaling and three-dimensional principal-components analysis score plots are employed. However, sometimes cluster membership for certain patterns is not always very clear or other ambiguities may arise; this paper describes cluster validation techniques using silhouettes and fuzzy clustering. The two methods operate in a complementary way: in some cases silhouettes are the most useful, and in others fuzzy clustering is more applicable. These procedures are available as options in the commercial computer programPolySNAP.

Two-stage hierarchical video summary extraction to match low-level user browsing preferences

A compact summary of video that conveys visual content at various levels of detail enhances user interaction significantly. In this paper, we propose a two-stage framework to generate MPEG-7-compliant hierarchical key frame summaries of video sequences. At the first stage, which is carried out off-line at the time of content production, fuzzy clustering and data pruning methods are applied to given video segments to obtain a nonredundant set of key frames that comprise the finest level of the hierarchical summary. The number of key frames allocated to each shot or segment is determined dynamically and without user supervision through the use of cluster validation techniques. A coarser summary is generated on-demand in the second stage by reducing the number of key frames to match the low-level browsing preferences of a user. The proposed method has been validated by experimental results on a collection of video programs.

Visual cluster validity for prototype generator clustering models

Conventional cluster validity techniques usually represent all the validity information available about a particular clustering by a single number. The display method introduced here uses images generated from the results of any prototype generator clustering algorithm to do cluster validation.

Fuzzy clustering of gait patterns of patients after ankle arthrodesis based on kinematic parameters

Kinematic parameters for 10 normal subjects and 10 patients with ankle arthrodesis are grouped using the fuzzy cluster paradigm. The features chosen for clustering are Euler angles of the sagittal plane in the hindfoot, the forefoot and combined hindfoot and forefoot joints. Gait patterns are identified using information provided by cluster validity techniques, giving three, three and two clusters for the hindfoot, forefoot and combined hindfoot and forefoot joints, respectively. The cluster centers represent distinct walking strategies adopted by normal subjects and patients after ankle arthrodesis. Utilizing angle values normalized by gait cycle, it is possible to classify any subject and to generate an individual's membership value for each of the clusters. The clinical utility of the fuzzy clustering approach is demonstrated with data for subjects with ankle arthrodesis, where changes in membership of the clusters provide an objective technique for measuring changes of gait pattern after ankle arthrodesis. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided in this study.

Resampling as a cluster validation technique in fMRI.

Exploratory, data-driven analysis approaches such as cluster analysis, principal component analysis, independent component analysis, or neural network-based techniques are complementary to hypothesis-led methods. They may be considered as hypothesis generating methods. The representative time courses they produce may be viewed as alternative hypotheses to the null hypothesis, i.e., "no activation." We present here a resampling technique to validate the results of exploratory fuzzy clustering analysis. In this case an alternative hypothesis is represented by a cluster centroid. For both simulated and in vivo functional magnetic resonance imaging data, we show that by permutation-based resampling, statistical significance may be computed for each voxel belonging to a cluster of interest without parametric distributional assumptions.

Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters.

Temporal-distance parameters for 88 children with the spastic diplegia form of cerebral palsy (CP) are grouped using the fuzzy clustering paradigm. The two features chosen for clustering are stride length and cadence which are normalized for age and leg length using a model based on a population of 68 neurologically intact children. Using information provided by the neurologically intact population and cluster validity techniques, five clusters for the children with cerebral palsy are identified. The five cluster centers represent distinct walking strategies adopted by children with cerebral palsy. Utilizing just four easily obtained parameters--stride length, cadence, leg length and age--and a small number of simple equations, it is possible to classify any child with spastic diplegia and to generate an individual's membership values for each of the five clusters. The clinical utility of the fuzzy clustering approach is demonstrated with pre- and post-operative test data for subjects with cerebral palsy (one neurosurgical and one orthopaedic) where changes in membership of the five clusters provide an objective technique for measuring improvement. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided here.