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PubMed2002

Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms.

This paper presents MRI segmentation techniques to differentiate abnormal and normal tissues in Ophthalmology using fuzzy clustering algorithms. Applying the best-known fuzzy c-means (FCM) clustering algorithm, a newly proposed algorithm, called an alternative fuzzy c-mean (AFCM), was used for MRI segmentation in Ophthalmology. These unsupervised segmentation algorithms can help Ophthalmologists to reduce the medical imaging noise effects originating from low resolution sensors and/or the structures that move during the data acquisition. They may be particularly helpful in the clinical oncological field as an aid to the diagnosis of Retinoblastoma, an inborn oncological disease in which symptoms usually show in early childhood. For the purpose of early treatment with radiotherapy and surgery, the newly proposed AFCM is preferred to provide more information for medical images used by Ophthalmologists. Comparisons between FCM and AFCM segmentations are made. Both fuzzy clustering segmentation techniques provide useful information and good results. However, the AFCM method has better detection of abnormal tissues than FCM according to a window selection. Overall, the newly proposed AFCM segmentation technique is recommended in MRI segmentation.

Algorithms↗
PubMed2006

Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple genomes.

Ortholog identification is a crucial first step in comparative genomics. Here, we present a rapid method of ortholog grouping which is effective enough to allow the comparison of many genomes simultaneously. The method takes as input all-against-all similarity data and classifies genes based on the traditional hierarchical clustering algorithm UPGMA. In the course of clustering, the method detects domain fusion or fission events, and splits clusters into domains if required. The subsequent procedure splits the resulting trees such that intra-species paralogous genes are divided into different groups so as to create plausible orthologous groups. As a result, the procedure can split genes into the domains minimally required for ortholog grouping. The procedure, named DomClust, was tested using the COG database as a reference. When comparing several clustering algorithms combined with the conventional bidirectional best-hit (BBH) criterion, we found that our method generally showed better agreement with the COG classification. By comparing the clustering results generated from datasets of different releases, we also found that our method showed relatively good stability in comparison to the BBH-based methods.

Algorithms↗
PubMed2001

Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data.

BACKGROUND: Artificial neural networks (ANNs) have been shown to be valuable in the analysis of analytical flow cytometric (AFC) data in aquatic ecology. Automated extraction of clusters is an important first stage in deriving ANN training data from field samples, but AFC data pose a number of challenges for many types of clustering algorithm. The fuzzy k-means algorithm recently has been extended to address nonspherical clusters with the use of scatter matrices. Four variants were proposed, each optimizing a different measure of clustering "goodness." METHODS: With AFC data obtained from marine phytoplankton species in culture, the four fuzzy k-means algorithm variants were compared with each other and with another multivariate clustering algorithm based on critical distances currently used in flow cytometry. RESULTS: One of the algorithm variants (adaptive distances, also known as the Gustafson--Kessel algorithm) was found to be robust and reliable, whereas the others showed various problems. CONCLUSIONS: The adaptive distances algorithm was superior in use to the clustering algorithms against which it was tested, but the problem of automatic determination of the number of clusters remains to be addressed.

Algorithms↗
PubMed1998

Magnetic resonance imaging studies with cluster algorithm for characterization of brain edema after controlled cortical impact injury (CCII).

Objective of this study was the characterization of traumatic brain injury induced by a "Controlled Cortical Impact" with magnetic resonance imaging techniques. The impact was applied to the intact dura of the left hemisphere in Sprague-Dawley rats. The pneumatic impactor was accelerated to a velocity of 7 m/s contusing the left temporo-parietal hemisphere to a depth of 2 mm. Posttraumatic hemispheric swelling and water content were determined gravimetrically, Evans Blue extravasation photometrically, and volume of ischemia by TTC-staining and planimetry. Magnetic resonance imaging was performed by a Bruker biospec 24/40, 90 min, 24 and 72 h post trauma using a T2w RARE sequence, a T1w sequence, before and after application of contrast agent, and a set of diffusion weighted images for calculation of ADC-maps. Data analysis was performed using a cluster algorithm enabling to interpret corresponding image pairs simultaneously. T2w imaging indicates the maximum edema about 24 h post trauma. Blood-brain barrier damage, detected by T1w imaging, is more predominant in the early posttraumatic phase. The cluster algorithm detects different edema components: from the necrotic core to the perifocal vasogenic rim. MRI in combination with the cluster algorithm will hopefully be a valuable tool in testing neuroprotective agents.

Animals↗
PubMed2004

A hierarchical clustering algorithm for MIMD architecture.

Hierarchical clustering is the most often used method for grouping similar patterns of gene expression data. A fundamental problem with existing implementations of this clustering method is the inability to handle large data sets within a reasonable time and memory resources. We propose a parallelized algorithm of hierarchical clustering to solve this problem. Our implementation on a multiple instruction multiple data (MIMD) architecture shows considerable reduction in computational time and inter-node communication overhead, especially for large data sets. We use the standard message passing library, message passing interface (MPI) for any MIMD systems.

Algorithms↗
PubMedDate not supplied

Implementing the Fisher's discriminant ratio in a k-means clustering algorithm for feature selection and data set trimming.

The Fisher's discriminant ratio has been used as a class separability criterion and implemented in a k-means clustering algorithm for performing simultaneous feature selection and data set trimming on a set of 221 HIV-1 protease inhibitors. The total number of molecular descriptors computed for each inhibitor is 43, and they are scaled to lie between 1 and 0 before being subjected to the feature selection process. Since the purpose is to select some of the most class sensitive descriptors, several feature evaluation indices such as the Shannon entropy, the linear regression of selected descriptors on the pKi of selected inhibitors, and a stepwise variable selection program are used to filter them. While the Shannon entropy provides the information content for each descriptor computed, more class sensitive descriptors are searched by both the linear regression and stepwise variable selection procedures. The inhibitors are divided into several different numbers of classes. They are subsequently divided into five classes due to the fact that the best feature selection result is obtained by the division. Most of the good features selected are the topological descriptors, and they are correlated well with the pKi values. The outliers or the inhibitors with less class-sensitive descriptor values computed for each selected descriptor are identified and gathered by the k-means clustering algorithm. These are the trimmed inhibitors, while the remaining ones are retained or selected. We find that 44% or 98 inhibitors can be retained when the number of good descriptors selected for clustering is three. The descriptor values of these selected inhibitors are far more class sensitive than the original ones as evidenced by substantial increasing in statistical significance when they are subjected to both the SYBYL CoMFA PLS and Cerius2 PLS regression analyses.

Journal Article↗
PubMed2005

Motion repulsion arises from stimulus statistics when analyzed with a clustering algorithm.

Motion repulsion is the perceived enlargement of the angle between the directions of motion of two transparently moving patterns. An explanation of this illusion has long been sought for in the neural circuitry of the brain. We show that motion repulsion already arises from the statistical properties of the motion transparency problem when analyzed with a clustering algorithm.

Adaptation, Ocular↗
PubMed2005

Survey of clustering algorithms.

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

Algorithms↗
PubMed2005

Online clustering algorithms for radar emitter classification.

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.

Algorithms↗
PubMed2003

An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation.

An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.

Algorithms↗
PubMedDate not supplied

End-to-end linkage (EEL) clustering algorithm: a study on the distribution of Meissner corpuscles in the skin.

A novel hierarchical clustering algorithm was applied to the distribution of Meissner corpuscles in the skin of mammals. This method, called end-to-end linkage (EEL), is useful for grouping data that consists of chain-like contingencies in the multivariable space. Unlike the traditional techniques which uncover hyperspherical clusters (e.g. single linkage), EEL considers the shortest distance between the predefined end pairs of the two clusters as an inter-group distance. This scheme allows characterizing the internal structure of data better than other hierarchical techniques. The anatomical data used in the case study is important for studying the sense of touch. The results show a substantial improvement over the traditional single-linkage method. On average, the number of correctly classified corpuscles is increased to twice the number identified by the single-linkage method. EEL can also be used for analyzing other sensory modalities where geometric relationships need to be explored. In addition, the report contains corpuscle density and epidermal-ridge width data obtained from several species.

Algorithms↗
PubMed2004

Unsupervised clustering algorithm for N-dimensional data.

Cluster analysis is an important tool for classifying data. Established techniques include k-means and k-median cluster analysis. However, these methods require the user to provide a priori estimations of the number of clusters and their approximate location in the parameter space. Often these estimations can be made based on some prior understanding about the nature of the data. Alternatively, the user makes these estimations based on visualization of the data. However, the latter is problematic in data sets with large numbers of dimensions. Presented here is an algorithm that can automatically provide these estimates without human intervention based on the inherent structure of the data set. The number of dimensions does not limit it.

Algorithms↗
PubMed2005

A robust information clustering algorithm.

We focus on the scenario of robust information clustering (RIC) based on the minimax optimization of mutual information (MI). The minimization of MI leads to the standard mass-constrained deterministic annealing clustering, which is an empirical risk-minimization algorithm. The maximization of MI works out an upper bound of the empirical risk via the identification of outliers (noisy data points). Furthermore, we estimate the real risk VC-bound and determine an optimal cluster number of the RIC based on the structural risk-minimization principle. One of the main advantages of the minimax optimization of MI is that it is a nonparametric approach, which identifies the outliers through the robust density estimate and forms a simple data clustering algorithm based on the square error of the Euclidean distance.

Algorithms↗
PubMedDate not supplied

A fast clustering algorithm for analyzing highly similar compounds of very large libraries.

As a result of the recent developments of high-throughput screening in drug discovery, the number of available screening compounds has been growing rapidly. Chemical vendors provide millions of compounds; however, these compounds are highly redundant. Clustering analysis, a technique that groups similar compounds into families, can be used to analyze such redundancy. Many available clustering methods focus on accurate classification of compounds; they are slow and are not suitable for very large compound libraries. Here is described a fast clustering method based on an incremental clustering algorithm and the 2D fingerprints of compounds. This method can cluster a very large data set with millions of compounds in hours on a single computer. A program implemented with this method, called cd-hit-fp, is available from http://chemspace.org.

Algorithms↗
PubMed2005

Cluster algorithm for nonadditive hard-core mixtures.

In this paper, we present a cluster algorithm for the numerical simulations of nonadditive hard-core mixtures. This algorithm allows one to simulate and equilibrate systems with a number of particles two orders of magnitude larger than previous simulations. The phase separation for symmetric binary mixtures is studied for different nonadditivities as well as for the Widom-Rowlinson model [B. Widom and J. S. Rowlinson, J. Chem. Phys. 52, 1670 (1970)] in two and three dimensions. The critical densities are determined from finite size scaling. The critical exponents for all the nonadditivities are consistent with the Ising universality class.

Journal Article↗
PubMed2005

Improved K-means clustering algorithm for exploring local protein sequence motifs representing common structural property.

Information about local protein sequence motifs is very important to the analysis of biologically significant conserved regions of protein sequences. These conserved regions can potentially determine the diverse conformation and activities of proteins. In this work, recurring sequence motifs of proteins are explored with an improved K-means clustering algorithm on a new dataset. The structural similarity of these recurring sequence clusters to produce sequence motifs is studied in order to evaluate the relationship between sequence motifs and their structures. To the best of our knowledge, the dataset used by our research is the most updated dataset among similar studies for sequence motifs. A new greedy initialization method for the K-means algorithm is proposed to improve traditional K-means clustering techniques. The new initialization method tries to choose suitable initial points, which are well separated and have the potential to form high-quality clusters. Our experiments indicate that the improved K-means algorithm satisfactorily increases the percentage of sequence segments belonging to clusters with high structural similarity. Careful comparison of sequence motifs obtained by the improved and traditional algorithms also suggests that the improved K-means clustering algorithm may discover some relatively weak and subtle sequence motifs, which are undetectable by the traditional K-means algorithms. Many biochemical tests reported in the literature show that these sequence motifs are biologically meaningful. Experimental results also indicate that the improved K-means algorithm generates more detailed sequence motifs representing common structures than previous research. Furthermore, these motifs are universally conserved sequence patterns across protein families, overcoming some weak points of other popular sequence motifs. The satisfactory result of the experiment suggests that this new K-means algorithm may be applied to other areas of bioinformatics research in order to explore the underlying relationships between data samples more effectively.

Algorithms↗
PubMed2000

CLICK: a clustering algorithm with applications to gene expression analysis.

Novel DNA microarray technologies enable the monitoring of expression levels of thousands of genes simultaneously. This allows a global view on the transcription levels of many (or all) genes when the cell undergoes specific conditions or processes. Analyzing gene expression data requires the clustering of genes into groups with similar expression patterns. We have developed a novel clustering algorithm, called CLICK, which is applicable to gene expression analysis as well as to other biological applications. No prior assumptions are made on the structure or the number of the clusters. The algorithm utilizes graph-theoretic and statistical techniques to identify tight groups of highly similar elements (kernels), which are likely to belong to the same true cluster. Several heuristic procedures are then used to expand the kernels into the full clustering. CLICK has been implemented and tested on a variety of biological datasets, ranging from gene expression, cDNA oligo-fingerprinting to protein sequence similarity. In all those applications it outperformed extant algorithms according to several common figures of merit. CLICK is also very fast, allowing clustering of thousands of elements in minutes, and over 100,000 elements in a couple of hours on a regular workstation.

Algorithms↗
PubMed1989

Six clustering algorithms applied to the WAIS-R: the problem of dissimilar cluster results.

Clusterings of the Wechsler Adult Intelligence Scale-Revised subtests were obtained from the application of six hierarchical clustering methods (N = 113). These sets of clusters were compared for similarities using the Rand index. The calculated indices suggested similarities of cluster group membership between the Complete Linkage and Centroid methods; Complete Linkage and Ward's methods; Centroid and Ward's methods; and Single Linkage and Average Linkage Between Groups methods. Cautious use of single clustering methods is implied, though the authors suggest some advantages of knowing specific similarities and differences. If between-method comparisons consistently reveal similar cluster membership, a choice could be made from those algorithms that tend to produce similar partitions, thereby enhancing cluster interpretation.

Accidents, Traffic↗