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A CLUE for CLUster Ensembles
 Journal of Statistical Software
, 2005
"... Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structu ..."
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Cited by 31 (7 self)
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Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and “secondary ” clusterings. 1
Swarm Intelligence Algorithms for Data Clustering
 IN SOFT COMPUTING FOR KNOWLEDGE DISCOVERY AND DATA MINING BOOK, PART IV
"... Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge da ..."
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Cited by 26 (1 self)
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Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bioinspired algorithms, wellknown as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning any dataset into an optimal number of groups through one run of optimization. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm.
Clustering search algorithm for the capacitated centred clustering problem
 Computers & Operations Research
, 2010
"... The Capacitated Centred Clustering Problem (CCCP) consists in partitioning a set of n points into p disjoint clusters with a known capacity. Each cluster is specified by a centroid. The objective is to minimize the total dissimilarity within each cluster, such that a given capacity limit of the clus ..."
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Cited by 4 (0 self)
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The Capacitated Centred Clustering Problem (CCCP) consists in partitioning a set of n points into p disjoint clusters with a known capacity. Each cluster is specified by a centroid. The objective is to minimize the total dissimilarity within each cluster, such that a given capacity limit of the cluster is not exceeded. This paper presents a solution procedure for the CCCP, using the hybrid metaheuristic Clustering Search (CS), whose main idea is to identify promising areas of the search space by generating solutions through a metaheuristic and clustering them into groups that are then further explored with local search heuristics. Computational results in test problems of the literature show that the CS found a significant number of new best known solutions in reasonable computational times.
Pattern Clustering Using a Swarm Intelligence Approach
"... Summary. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of ..."
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Summary. Clustering aims at representing large datasets by a fewer number of prototypes or clusters. It brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. Data mining tasks, in these days, require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This, in turn, imposes severe computational requirements on the relevant clustering techniques. A family of bioinspired algorithms, wellknown as Swarm Intelligence (SI) has recently emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This chapter explores the role of SI in clustering different kinds of datasets. It finally describes a new SI technique for partitioning a linearly nonseparable dataset into an optimal number of clusters in the kernel induced feature space. Computer simulations undertaken in this research have also been provided to demonstrate the effectiveness of the proposed algorithm. 1
Neurofunctional Topography of the Human
"... r r Abstract: Much of what was assumed about the functional topography of the hippocampus was derived from a single case study over half a century ago. Given advances in the imaging sciences, a new era of discovery is underway, with potential to transform the understanding of healthy processing as w ..."
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r r Abstract: Much of what was assumed about the functional topography of the hippocampus was derived from a single case study over half a century ago. Given advances in the imaging sciences, a new era of discovery is underway, with potential to transform the understanding of healthy processing as well as the ability to treat disorders. Coactivationbased parcellation, a metaanalytic approach, and ultrahigh field, highresolution functional and structural neuroimaging to characterize the neurofunctional topography of the hippocampus was employed. Data revealed strong support for an evolutionarily preserved topography along the longaxis. Specifically, the left hippocampus was segmented into three distinct clusters: an emotional processing cluster supported by structural and functional connectivity to the amygdala and parahippocampal gyrus, a cognitive operations cluster, with functional connectivity to the anterior cingulate and inferior frontal gyrus, and a posterior perceptual cluster with distinct structural connectivity patterns to the occipital lobe coupled with functional connectivity to the precuneus and angular
RESEARCH ARTICLE Open Access Scalable analysis of Big pathology image
"... data cohorts using efficient methods and highperformance computing strategies ..."
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data cohorts using efficient methods and highperformance computing strategies
‘‘tarpey’ ’ 2008/5/24 page 199 #1 Statistical Modelling 2008; 8(2): 199218 Model misspecification: finite mixture or homogeneous?
, 2007
"... Abstract: A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous nonmixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous nonnormal densities. T ..."
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Abstract: A common problem in statistical modelling is to distinguish between finite mixture distribution and a homogeneous nonmixture distribution. Finite mixture models are widely used in practice and often mixtures of normal densities are indistinguishable from homogenous nonnormal densities. This paper illustrates what happens when the EM algorithm for normal mixtures is applied to a distribution that is a homogeneous nonmixture distribution. In particular, a populationbased EM algorithm for finite mixtures is introduced and applied directly to density functions instead of sample data. This algorithm is used to find finite mixture approximations to common homogeneous distributions. An example regarding the nature of a placebo response in drug treated depressed subjects is used to illustrate ideas. Key words: EM algorithm; finite mixture models; placebo response; principal points; skew normal distribution
Robust LongRange Coordination of Spontaneous Neural Activity in Waking, Sleep and Anesthesia
"... Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain’s largescale functional networks, the nature of the underlying neuroelectrical activity remains incompletely understood. ..."
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Although the emerging field of functional connectomics relies increasingly on the analysis of spontaneous fMRI signal covariation to infer the spatial fingerprint of the brain’s largescale functional networks, the nature of the underlying neuroelectrical activity remains incompletely understood. In part, this lack in understanding owes to the invasiveness of electrophysiological acquisition, the difficulty in their simultaneous recording over large cortical areas, and the absence of fully established methods for unbiased extraction of network information from these data. Here, we demonstrate a novel, datadriven approach to analyze spontaneous signal variations in electrocorticographic (ECoG) recordings from nearly entire hemispheres of macaque monkeys. Based on both broadband analysis and analysis of specific frequency bands, the ECoG signals were found to covary in patterns that resembled the fMRI networks reported in previous studies. The extracted patterns were robust against changes in consciousness associated with sleep and anesthesia, despite profound changes in intrinsic characteristics of the raw signals, including their spectral signatures. These results suggest that the spatial organization of largescale brain networks results from neural activity with a broadband spectral feature and is a core aspect of the brain’s physiology that does not depend on the state of consciousness.