Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
77
Accepted in International Journal of Business Intelligence and Data Mining, 2007 Fuzzy Clustering of Intuitionistic Fuzzy Data
"... Abstract: Clustering approaches organize a set of objects into groups whose members are proximate according to some similarity function defined on lowlevel features, assuming that their values are not subject to any kind of uncertainty. Furthermore, these methods assume that similarity is measured b ..."
Abstract
- Add to MetaCart
by accounting only the degree in which two entities are related, ignoring the hesitancy introduced by the degree in which they are unrelated. Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory
A novel multimodal probability model for cluster analysis
- In RSKT ’09: Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
, 2009
"... Abstract. Cluster analysis is a tool for data analysis. It is a method for finding clusters of a data set with most similarity in the same group and most dissimilarity between different groups. In general, there are two ways, mixture distributions and classification maximum likelihood method, to use ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
, to use probability models for cluster analysis. However, the cor-responding probability distributions to most clustering algorithms such as fuzzy c-means, possibilistic c-means, mode-seeking methods, etc., have not yet been found. In this paper, we construct a multimodal probabil-ity distribution model
A novel fuzzy clustering algorithm for the analysis of axillary lymph node tissue sections
, 2007
"... spectroscopic imaging has been used as a tool to detect the changes in cellular composition that may reflect the onset of a disease. This approach has been investigated as a mean of monitoring the change of the biochemical composition of cells and providing a diagnostic tool for various human cancer ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
, k-means and fuzzy c-means. In this study, we apply a FCM based clustering algorithm to obtain the best number of clusters as given by the minimum validity index value. This often results in an excessive number of clusters being created due to the complexity of this biochemical system. A novel method
Fuzzy Temporal Clustering Approach for E-Commerce Websites
"... Abstract—In this paper a novel approach for clustering of web logs data and to predict intelligent recommendations on the E-Commerce web sites is proposed so as to improve the marketing strategy and to improve customer loyalty. Fuzzy Temporal Clustering Approach (FTCA) performs clustering of the web ..."
Abstract
- Add to MetaCart
Abstract—In this paper a novel approach for clustering of web logs data and to predict intelligent recommendations on the E-Commerce web sites is proposed so as to improve the marketing strategy and to improve customer loyalty. Fuzzy Temporal Clustering Approach (FTCA) performs clustering
HUMAN MOVEMENT RECOGNITION USING FUZZY CLUSTERING AND DISCRIMINANT ANALYSIS
"... In this paper a novel method for human movement rep-resentation and recognition is proposed. A movement is regarded as a sequence of basic movement patterns, the so-called dynemes. Initially, the fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space, and then principal comp ..."
Abstract
- Add to MetaCart
In this paper a novel method for human movement rep-resentation and recognition is proposed. A movement is regarded as a sequence of basic movement patterns, the so-called dynemes. Initially, the fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space, and then principal
644 Novel approach to control false positive rate in fuzzy cluster analysis of fMRI
"... Fuzzy c-means (FCM) suffers from some limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results, when it is applied to the raw fMRI time series. Based on randomization, we developed a method to control the false ..."
Abstract
- Add to MetaCart
by an analysis of false positives in activation maps of resting-state fMRI data. Controlling the false positive rate allows comparison of different feature spaces and fuzzy clustering methods. A new feature space, in multi and scalar wavelet domain, is proposed for activation detection in fMRI to address
A Novel Inter-class Clustering Method for Image Reconstruction
"... Abstract- In this paper a novel inter class clustering method is developed for image reconstruction. The aim of image restoration is the removal of noise from images. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More sophist ..."
Abstract
- Add to MetaCart
Abstract- In this paper a novel inter class clustering method is developed for image reconstruction. The aim of image restoration is the removal of noise from images. The simplest possible approach for noise removal is various types of filters such as low-pass filters or median filters. More
Building Fuzzy Thematic Clusters and Mapping Them to Higher Ranks in a Taxonomy
"... Abstract. We present a novel methodology for the analysis of activities engaged in an organization such as the research conducted in a University department by mapping them to a related hierarchical taxonomy such as Classification of Computer Subjects by ACM (ACM-CCS). We start by collecting data of ..."
Abstract
-
Cited by 2 (2 self)
- Add to MetaCart
. Second, each cluster is mapped to higher ranks of the taxonomy in a parsimonious way. Each of the steps is formalized and solved in a novel way. We build fuzzy clusters of the taxonomy leaves according to the similarity between individual profiles by using a novel, additive spectral, fuzzy clustering
A Fuzzy-Statistics-Based Affinity Propagation Technique for Clustering in Multispectral Images
- IEEE Transactions on Geoscience and Remote Sensing
, 2010
"... Abstract—Due to a high number of spectral channels and a large information quantity, multispectral remote-sensing images are difficult to be classified with high accuracy and efficiency by conventional classification methods, particularly when training data are not available and when unsupervised cl ..."
Abstract
-
Cited by 4 (0 self)
- Add to MetaCart
clustering tech-niques should be considered for data analysis. In this paper, we propose a novel image clustering method [called fuzzy-statistics-based affinity propagation (FS-AP)] which is based on a fuzzy statistical similarity measure (FSS) to extract land-cover information in multispectral imagery. AP
Novel Feature Extraction Technique for Fuzzy Relational Clustering of a Flexible Dopamine Reuptake Inhibitor
- Journal of chemical information and computer Sciences
, 2005
"... This paper describes a novel clustering methodology for classifying over 700 conformations of a flexible analogue of GBR 12909, a dopamine reuptake inhibitor that has completed phase I clinical trials as a treatment for cocaine abuse. The major aspect of the clustering methodology includes an effici ..."
Abstract
-
Cited by 1 (0 self)
- Add to MetaCart
This paper describes a novel clustering methodology for classifying over 700 conformations of a flexible analogue of GBR 12909, a dopamine reuptake inhibitor that has completed phase I clinical trials as a treatment for cocaine abuse. The major aspect of the clustering methodology includes
Results 1 - 10
of
77
