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Results for “Clustering Algorithms”

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PubMed2005

Scalable model-based clustering for large databases based on data summarization.

The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources such as memory and computation time. In this paper, two scalable clustering algorithms, bEMADS and gEMADS, are presented based on the Gaussian mixture model. Both summarize data into subclusters and then generate Gaussian mixtures from their data summaries. Their core algorithm, EMADS, is defined on data summaries and approximates the aggregate behavior of each subcluster of data under the Gaussian mixture model. EMADS is provably convergent. Experimental results substantiate that both algorithms can run several orders of magnitude faster than expectation-maximization with little loss of accuracy.

Algorithms↗

PubMed2006

Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization.

We provide evidence that nonlinear dimensionality reduction, clustering, and data set parameterization can be solved within one and the same framework. The main idea is to define a system of coordinates with an explicit metric that reflects the connectivity of a given data set and that is robust to noise. Our construction, which is based on a Markov random walk on the data, offers a general scheme of simultaneously reorganizing and subsampling graphs and arbitrarily shaped data sets in high dimensions using intrinsic geometry. We show that clustering in embedding spaces is equivalent to compressing operators. The objective of data partitioning and clustering is to coarse-grain the random walk on the data while at the same time preserving a diffusion operator for the intrinsic geometry or connectivity of the data set up to some accuracy. We show that the quantization distortion in diffusion space bounds the error of compression of the operator, thus giving a rigorous justification for k-means clustering in diffusion space and a precise measure of the performance of general clustering algorithms.

Algorithms↗

PubMed2006

Dynamic characterization of cluster structures for robust and inductive support vector clustering.

A topological and dynamical characterization of the cluster structures described by the support vector clustering is developed. It is shown that each cluster can be decomposed into its constituent basin level cells and can be naturally extended to an enlarged clustered domain, which serves as a basis for inductive clustering. A simplified weighted graph preserving the topological structure of the clusters is also constructed and is employed to develop a robust and inductive clustering algorithm. Simulation results are given to illustrate the robustness and effectiveness of the proposed method.

Algorithms↗

PubMed2006

Rough-fuzzy collaborative clustering.

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.

Algorithms↗

PubMed2005

Shortest triplet clustering: reconstructing large phylogenies using representative sets.

BACKGROUND: Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics. RESULTS: We propose a new distance-based clustering method, the shortest triplet clustering algorithm (STC), to reconstruct phylogenies. The main idea is the introduction of a natural definition of so-called k-representative sets. Based on k-representative sets, shortest triplets are reconstructed and serve as building blocks for the STC algorithm to agglomerate sequences for tree reconstruction in O(n2) time for n sequences. Simulations show that STC gives better topological accuracy than other tested methods that also build a first starting tree. STC appears as a very good method to start the tree reconstruction. However, all tested methods give similar results if balanced nearest neighbor interchange (BNNI) is applied as a post-processing step. BNNI leads to an improvement in all instances. The program is available at http://www.bi.uni-duesseldorf.de/software/stc/. CONCLUSION: The results demonstrate that the new approach efficiently reconstructs phylogenies for large data sets. We found that BNNI boosts the topological accuracy of all methods including STC, therefore, one should use BNNI as a post-processing step to get better topological accuracy.

Algorithms↗

PubMed2006

Biclustering of gene expression data by Non-smooth Non-negative Matrix Factorization.

BACKGROUND: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. RESULTS: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression profiles. In all cases the method was able to identify localized features related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The uncovered structures showed a clear biological meaning in terms of relationships among functional annotations of genes and the phenotypes or physiological states of the associated conditions. CONCLUSION: The proposed approach can be a useful tool to analyze large and heterogeneous gene expression datasets. The method is able to identify complex relationships among genes and conditions that are difficult to identify by standard clustering algorithms.

Algorithms↗

PubMed2006

Microsatellite diversity and broad scale geographic structure in a model legume: building a set of nested core collection for studying naturally occurring variation in Medicago truncatula.

BACKGROUND: Exploiting genetic diversity requires previous knowledge of the extent and structure of the variation occurring in a species. Such knowledge can in turn be used to build a core-collection, i.e. a subset of accessions that aim at representing the genetic diversity of this species with a minimum of repetitiveness. We investigate the patterns of genetic diversity and population structure in a collection of 346 inbred lines representing the breadth of naturally occurring diversity in the Legume plant model Medicago truncatula using 13 microsatellite loci distributed throughout the genome. RESULTS: We confirm the uniqueness of all these genotypes and reveal a large amount of genetic diversity and allelic variation within this autogamous species. Spatial genetic correlation was found only for individuals originating from the same population and between neighbouring populations. Using a model-based clustering algorithm, we identified four main genetic clusters in the set of individuals analyzed. This stratification matches broad geographic regions. We also identified a set of "admixed" individuals that do not fit with this population structure scheme. CONCLUSION: The stratification inferred is discussed considering potential historical events like expansion, refuge history and admixture between neighbouring groups. Information on the allelic richness and the inferred population structure are used to build a nested core-collection. The set of inbred lines and the core collections are publicly available and will help coordinating efforts for the study of naturally occurring variation in the growing Medicago truncatula community.

DNA, Plant↗

PubMed2002

Factor analysis of cluster-specific gene expression levels from cDNA microarrays.

The ever-increasing use of cDNA microarrays in medical research will require the development of new algorithms designed specifically for desktop analysis of potentially large genetic data sets. This paper describes the CLUSFAVOR algorithm (CLUSter and Factor Analysis Using Varimax Orthogonal Rotation) for performing cluster and factor analysis of gene expression data obtained from cDNA microarrays. A unique feature of the CLUSFAVOR algorithm is that a user can perform cluster analysis, view dendograms, and run factor analysis on cluster-specific genes selected interactively within a single executable program. CLUSFAVOR also performs a varimax orthogonal rotation on the extracted factors to increase parsimony in the loadings, revealing unique expression profiles for genes and expressed sequence tags (ESTs) for which pathway and function information is unknown. Microarray data used by the program must be stored and input from a disk file. Optional output contains matrices for the input data, standardized data, distance matrices, factor loadings, eigenvalues, eigenvectors, and percentage of total variation for genes within a cluster. Color cluster image displays containing gene expression levels, dendograms for arrays and genes, and factor loadings are displayed for the entire group of genes and arrays analyzed as well as selected genes within a cluster. Cluster images are also exported to JPG and linked to HTML files for viewing.

Algorithms↗

PubMed2005

EM clustering analysis of diabetes patients basic diagnosis index.

Cluster analysis can group similar instances into same group and different instances into different groups. It assigns classes to samples without known the classes in advance. EM clustering algorithm can find number of distributions of generating data and build "mixture models". It identifies groups that are either overlapping or varying sizes and shapes. In this project, by using EM in Weka system, diabetes patient basic diagnosis index data have been analyzed for clustering.

Algorithms↗

PubMed2004

Primer design and marker clustering for multiplex SNP-IT primer extension genotyping assay using statistical modeling.

MOTIVATION: The optimization of the primer design is critical for the development of high-throughput SNP genotyping methods. Recently developed statistical models of the SNP-IT primer extension genotyping reaction allow further improvement of primer quality for the assay. RESULTS: Here we describe how the statistical models can be used to improve primer design for the assay. We also show how to optimize clustering of the SNP markers into multiplex panels using statistical model for multiplex SNP-IT. The primer set failure probability calculated by a model is used as a minimization function for both primer selection and primers clustering. Three clustering algorithms for the multiplex genotyping SNP-IT assay are described and their relative performance is evaluated. We also describe the approaches to improve the speed of primer design and clustering calculations when using the statistical models. Our clustering decreases the average failure probability of the marker set by 7-25%. The experimental marker failure rate in the multiplex reaction was reduced dramatically and success rate can be achieved as high as 96%. AVAILABILITY: The primer design using statistical models is freely available from www.autoprimer.com.

Algorithms↗

PubMed2006

Cluster-based analysis of FMRI data.

We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation. The advantages of this analysis over previous ones are both conceptual and statistical. Recognizing that the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data. Testing the cluster units has a two-fold statistical advantage over testing each voxel separately: the signal to noise ratio within the unit tested is higher, and the number of hypotheses tests compared is smaller. We suggest controlling FDR on clusters, i.e., the proportion of clusters rejected erroneously out of all clusters rejected and explain the meaning of controlling this error rate. We introduce the powerful adaptive procedure to control the FDR on clusters. We apply our cluster-based analysis (CBA) to both an event-related and a block design fMRI vision experiment and demonstrate its increased power over voxel-by-voxel analysis in these examples as well as in simulations.

Algorithms↗

PubMedDate not supplied

Model parameter estimation and analysis: understanding parametric structure.

We developed three algorithms to facilitate an analysis of the parameter combinations (PASS points) that fit experimental data to a desired degree of accuracy. The clustering algorithm separates PASS points into clusters (PASS clusters) as a preliminary step for the following geometrical parametric analyses. The PASS region reconstruction algorithm defines the space of a PASS cluster to allow further parametric structural analysis. The feasible parameter space expansion algorithm produces a complete PASS cluster to be used for model predictions to evaluate the effects of variability and uncertainty. These algorithms are demonstrated using two pharmacokinetic models; a single compartment model for procainamide and a three-compartment physiologically based model for benzene. We found a more thorough representation of the parameter space than previously considered. Thus, we obtained model predictions that describe better the variability in population responses. In addition, we also parametrically identified a subpopulation that may have a higher risk for cancer.

Algorithms↗

PubMed2004

Organizing literature information for clinical decision support.

Answers to clinical questions occurring during healthcare practitioner/patient interaction can be often found in National Library of Medicine's (NLM) databases. The recent advances in wireless handheld computers promise to make them a widely used tool to deliver needed information to the practitioner at the point of service. This paper addresses challenges in organizing and presenting information obtained from NLM's MEDLINE database of indexed citations in a way that will help practitioners reduce literature search time on handheld computers. We study two clustering algorithms and two methods of labeling document clusters.

Algorithms↗

PubMed1999

A reinforcement learning approach to online clustering.

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) algorithm is proposed that constitutes a reinforcement-based adaptation of learning vector quantization (LVQ) with enhanced clustering capabilities. In addition, we suggest extensions of RGCL and LVQ that are characterized by the property of sustained exploration and significantly improve the performance of those algorithms, as indicated by experimental tests on well-known data sets.

Algorithms↗

PubMed2001

Principal component analysis for clustering gene expression data.

MOTIVATION: There is a great need to develop analytical methodology to analyze and to exploit the information contained in gene expression data. Because of the large number of genes and the complexity of biological networks, clustering is a useful exploratory technique for analysis of gene expression data. Other classical techniques, such as principal component analysis (PCA), have also been applied to analyze gene expression data. Using different data analysis techniques and different clustering algorithms to analyze the same data set can lead to very different conclusions. Our goal is to study the effectiveness of principal components (PCs) in capturing cluster structure. Specifically, using both real and synthetic gene expression data sets, we compared the quality of clusters obtained from the original data to the quality of clusters obtained after projecting onto subsets of the principal component axes. RESULTS: Our empirical study showed that clustering with the PCs instead of the original variables does not necessarily improve, and often degrades, cluster quality. In particular, the first few PCs (which contain most of the variation in the data) do not necessarily capture most of the cluster structure. We also showed that clustering with PCs has different impact on different algorithms and different similarity metrics. Overall, we would not recommend PCA before clustering except in special circumstances.

Algorithms↗

PubMed1996

Multisolutional clustering and quantization algorithm (MCQ).

We have developed a novel clustering and quantization algorithm that allows the user to create multiple one-to-one correspondences between the actual data and its transformed (clustered and quantized) values, based on the user's hypothesis regarding the nature of the classification task. The types of problems for which the algorithm can be beneficial are discussed. We report experiments employing simulated and real data that suggest the proposed algorithm may be useful in neural network analysis of various phenomena in medicine and biology.

Algorithms↗

PubMed2004

Predicting allergenic proteins using wavelet transform.

MOTIVATION: With many transgenic proteins introduced today, the ability to predict their potential allergenicity has become an important issue. Previous studies were based on either sequence similarity or the protein motifs identified from known allergen databases. The similarity-based approaches, although being able to produce high recalls, usually have low prediction precisions. Previous motif-based approaches have been shown to be able to improve the precisions on cross-validation experiments. In this study, a system that combines the advantages of similarity-based and motif-based prediction is described. RESULTS: The new prediction system uses a clustering algorithm that groups the known allergenic proteins into clusters. Proteins within each cluster are assumed to carry one or more common motifs. After a multiple sequence alignment, proteins in each cluster go through a wavelet analysis program whereby conserved motifs will be identified. A hidden Markov model (HMM) profile will then be prepared for each identified motif. The allergens that do not appear to carry detectable allergen motifs will be saved in a small database. The allergenicity of an unknown protein may be predicted by comparing it against the HMM profiles, and, if no matching profiles are found, against the small allergen database by BLASTP. Over 70% of recall and over 90% of precision were observed using cross-validation experiments. Using the entire Swiss-Prot as the query, we predicted about 2000 potential allergens. AVAILABILITY: The software is available upon request from the authors.

Algorithms↗

PubMed1993

An examination of cluster-based classification schemes for DUI offenders.

This study examines the utility of cluster-based classification schemes for DUI offenders. Variables from previous empirical typologies and multiple domains were used in a series of cluster analyses in order to examine replicability across independent samples, across clustering algorithms and across sets of psychometric indicators. The arbitrary nature of cluster solutions and the external validity of cluster-based schemes, with respect to an outcome criterion constructed synthetically from earlier research, was examined. Only three of eight cluster techniques (Ward's, K means, and complete linkage) yielded meaningful results. For these techniques, replicability was poor across samples, algorithms and sets of psychometric indicators. Results indicated that identified clusters were arbitrary. Although external validity analysis yielded positive results, the possibility that external validity was a function of underlying dimensions was discussed, as were other implications of the findings.

Adult↗