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Partitioning Around Medoids (PAM) Cluster Model

An Accurate Grid  based PAM Clustering Method for Large Dataset

An Accurate Grid based PAM Clustering Method for Large Dataset

... data partitioning is to take a conceptual point of view that identifies the cluster with a certain model whose unknown parameters have to be ...optimization partitioning algorithms are ...

6

Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms

... This study experiment with two partitioning-based algorithms and a probabilistic model-based algorithm, the clusters formed are shown in Figures 5, 6, and 7. The algorithms operate under similar parameter ...

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GARCH-based robust clustering of time series

GARCH-based robust clustering of time series

... so-called model-based approach to time series clustering and using a partitioning around medoids ...Each model neutralizes the negative effects of the outliers in the clustering process ...

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A MapReduce-based distributed SVM algorithm for automatic image annotation

A MapReduce-based distributed SVM algorithm for automatic image annotation

... as partitioning the input data, scheduling the program’s execution across a cluster of participating nodes, handling node failures, and managing the required network ...

11

Evidential relational clustering using medoids

Evidential relational clustering using medoids

... of pairs in the same group of the clustering benchmark, and ER is the fraction of specifically retrieved instances (grouped into an identical specific cluster) out of these relevant pairs. Value a ∗ (respectively, ...

9

Principal Component Analysis Based Transformation for Privacy Preserving in Data Stream Mining

Principal Component Analysis Based Transformation for Privacy Preserving in Data Stream Mining

... But nowadays, in the field of information processing, an emergence of applications that do not fit this data model [2] Instead, information naturally occurs in the form of a sequence (stream) of data values. A ...

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Multi-stream continuous hidden Markov models with application to landmine detection

Multi-stream continuous hidden Markov models with application to landmine detection

... and model fusion (early/intermediate integration) ...HMM model is trained ...category, model fusion, a more complex HMM model than the standard one is ...

23

Performance Measure of Hard c-means,Fuzzy          c-means and Alternative c-means Algorithms

Performance Measure of Hard c-means,Fuzzy c-means and Alternative c-means Algorithms

... Abstract: Clustering analysis can be used to classify the objects into subsets with similar attributes. The main objective of clustering techniques is to group the data points in a multi-attribute dataset such that the ...

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K-Medoids Clustering  Using Partitioning Around Medoids for Performing Face Recognition

K-Medoids Clustering Using Partitioning Around Medoids for Performing Face Recognition

... Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying ...

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

... optimization model involving two sets of unknown, ...the model by iteratively updating membership function and prototype sets until the optimum solution is ...

5

Adaptive Virtual Partitioning for OLAP Query Processing in a Database Cluster

Adaptive Virtual Partitioning for OLAP Query Processing in a Database Cluster

... We now describe what happens at each cluster node during query processing. Figure 3 illustrates the main node components and their interaction. The main components that run in each cluster node are the Node ...

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Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

... Studies on dividing a given geographical area into some functional regions or uniform regions have been conducted in geography. Though notion of these regions is some- what different from our functional clusters, they ...

14

A Comparative study on data mining clustering...

A Comparative study on data mining clustering...

... clusters. Partitioning the data recursively and generating clusters by using top down or bottom up approach is a way of mining huge datasets by clustering method ...a cluster and recursively merging data ...

5

Article Description

Article Description

... a cluster should be as similar as possible and documents in one cluster should be as dissimilar as possible from documents in other ...determine cluster membership In supervised classification the ...

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A Novel Partitioning Driven Differential Evolution based Epileptic Seizure Cluster Analysis

A Novel Partitioning Driven Differential Evolution based Epileptic Seizure Cluster Analysis

... The cluster process aims to arrange a collection of data samples into different clusters, in such a way that the objects belonging to a cluster are too close to one another than the objects belonging to ...

6

Cluster Analysis of Temporal Data using Maximum Likelihood Estimation

Cluster Analysis of Temporal Data using Maximum Likelihood Estimation

... Some algorithms directly work on temporal data. Using temporal similarity measure, cluster analysis is done. Using dynamic time warping or dynamic models, clustering analysis provides finding similarity between ...

5

Finding Similar Documents Using Different Clustering Techniques

Finding Similar Documents Using Different Clustering Techniques

... on partitioning data points into k clusters using the concept of ...The cluster centroid is the mean value of the data points within a ...as cluster centroids. 2) the similarity of each data point to ...

7

On the Random Cluster Model

On the Random Cluster Model

... the model that is the basis of many subsequent ...Random Cluster Model, where we extend the Random Cluster Model to an infinite lattice Z d ...

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Efficient analysis of data streams

Efficient analysis of data streams

... cannot cluster data sets well with large differences in densities, due to global ...involves partitioning the feature space into dense grid cells instead of ...

132

Mapping Diversity of Publication Patterns in the Social Sciences and Humanities: An Approach Making Use of Fuzzy Cluster Analysis

Mapping Diversity of Publication Patterns in the Social Sciences and Humanities: An Approach Making Use of Fuzzy Cluster Analysis

... hard partitioning, the publication practices of sociologists show a distinctive pattern, with ...to Cluster One (international journals and English) and 52.3% to Cluster Two (national journals and ...

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