1,085 research outputs found
Dynamic Fuzzy c-Means (dFCM) Clustering and its Application to Calorimetric Data Reconstruction in High Energy Physics
In high energy physics experiments, calorimetric data reconstruction requires
a suitable clustering technique in order to obtain accurate information about
the shower characteristics such as position of the shower and energy
deposition. Fuzzy clustering techniques have high potential in this regard, as
they assign data points to more than one cluster,thereby acting as a tool to
distinguish between overlapping clusters. Fuzzy c-means (FCM) is one such
clustering technique that can be applied to calorimetric data reconstruction.
However, it has a drawback: it cannot easily identify and distinguish clusters
that are not uniformly spread. A version of the FCM algorithm called dynamic
fuzzy c-means (dFCM) allows clusters to be generated and eliminated as
required, with the ability to resolve non-uniformly distributed clusters. Both
the FCM and dFCM algorithms have been studied and successfully applied to
simulated data of a sampling tungsten-silicon calorimeter. It is seen that the
FCM technique works reasonably well, and at the same time, the use of the dFCM
technique improves the performance.Comment: 15 pages, 10 figures. It is accepted for publication in NIM
Extended Fuzzy Clustering Algorithms
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. Ithas been applied successfully in various fields including finance and marketing. Despitethe successful applications, there are a number of issues that must be dealt with in practicalapplications of fuzzy clustering algorithms. This technical report proposes two extensionsto the objective function based fuzzy clustering for dealing with these issues. First, the(point) prototypes are extended to hypervolumes whose size is determined automaticallyfrom the data being clustered. These prototypes are shown to be less sensitive to a biasin the distribution of the data. Second, cluster merging by assessing the similarity amongthe clusters during optimization is introduced. Starting with an over-estimated number ofclusters in the data, similar clusters are merged during clustering in order to obtain a suitablepartitioning of the data. An adaptive threshold for merging is introduced. The proposedextensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resultingextended algorithms are given. The properties of the new algorithms are illustrated invarious examples.fuzzy clustering;cluster merging;similarity;volume prototypes
Fast Color Quantization Using Weighted Sort-Means Clustering
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
Fuzzy Clustering for Image Segmentation Using Generic Shape Information
The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object's shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects
Fuzzy clustering and fuzzy c-means partition cluster analysis and validation studies on a subset of citescore dataset
A hard partition clustering algorithm assigns equally distant points to one of the clusters, where each datum has the probability to appear in simultaneous assignment to further clusters. The fuzzy cluster analysis assigns membership coefficients of data points which are equidistant between two clusters so the information directs have a place toward in excess of one cluster in the meantime. For a subset of CiteScore dataset, fuzzy clustering (fanny) and fuzzy c-means (fcm) algorithms were implemented to study the data points that lie equally distant from each other. Before analysis, clusterability of the dataset was evaluated with Hopkins statistic which resulted in 0.4371, a value < 0.5, indicating that the data is highly clusterable. The optimal clusters were determined using NbClust package, where it is evidenced that 9 various indices proposed 3 cluster solutions as best clusters. Further, appropriate value of fuzziness parameter m was evaluated to determine the distribution of membership values with variation in m from 1 to 2. Coefficient of variation (CV), also known as relative variability was evaluated to study the spread of data. The time complexity of fuzzy clustering (fanny) and fuzzy c-means algorithms were evaluated by keeping data points constant and varying number of clusters
Extended Fuzzy Clustering Algorithms
Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It
has been applied successfully in various fields including finance and marketing. Despite
the successful applications, there are a number of issues that must be dealt with in practical
applications of fuzzy clustering algorithms. This technical report proposes two extensions
to the objective function based fuzzy clustering for dealing with these issues. First, the
(point) prototypes are extended to hypervolumes whose size is determined automatically
from the data being clustered. These prototypes are shown to be less sensitive to a bias
in the distribution of the data. Second, cluster merging by assessing the similarity among
the clusters during optimization is introduced. Starting with an over-estimated number of
clusters in the data, similar clusters are merged during clustering in order to obtain a suitable
partitioning of the data. An adaptive threshold for merging is introduced. The proposed
extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting
extended algorithms are given. The properties of the new algorithms are illustrated in
various examples
A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering
© 2013 IEEE. Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering
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