Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

Sivarathri, Srinivas; Govardhan, A.

doi:10.5121/ijdkp.2014.4502

Cited by 21 publications

(18 citation statements)

References 12 publications

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“…FCM is an algorithm based on more iterative fuzzy calculations, so its execution was found comparatively higher as it is expected. Similar results were reported by Panda et al (2012) for Iris, Wine and Lens datasets; by Jipkate & Gohokar (2012) for segmentation of images; by Ghosh & Dubey (2013) for Iris dataset; by Bora & Gupta (2014) for Iris dataset; by Sivarathri & Govardhan (2014) for diabetes data; and by Madhukumar & Santhiyakumari (2015) for brain MR images data.…”

Section: Discussionsupporting

confidence: 74%

“…Testing the proposed approaches on real data with different ellipsoidal shapes of clusters may be helpful for a precise decision between the algorithms. Sivarathri & Govardhan (2014) revealed that FCM is better than KM in term of accuracy of clusters on the diabetes dataset obtained from the UCI repository. However, in our study, neither KM nor FCM were successful to find the concave and other kind of arbitrary shaped clusters when they are not well separated.…”

Section: Discussionmentioning

confidence: 99%

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Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Cebecí

Yildiz²

2015

JAI

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In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accuracy with the FCM. Moreover the KM with multiple starts was extremely superior to the FCM in computing time in all datasets analyzed. Therefore, when well separated cluster structures spreading with regular patterns do exist in datasets the KM with multiple starts was recommended for cluster analysis because of its comparable accuracy and runtime performances.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Cebecí

Yildiz²

2015

JAI

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“…FCM is widely applied as the clustering and classification method in many fields such as data mining, image processing, and bioinformatics. Research often compares K-means, FCM, and other partitive clustering algorithms in data classification (see, eg, the papers by Gosh and Dubey, 14 Velmurugan and Santhanam, 15 Yin et al, 16 and Sivarathri and Govardhan, 17 ). Results obtained on different datasets show that although FCM requires necessarily more computation time than K-Means clustering, it produces better results than K-means in terms of accuracy of clusters.…”

Section: The Efcm Algorithmmentioning

confidence: 99%

A lightweight clustering–based approach to discover different emotional shades from social message streams

2019

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With the explosion of social media, automatic analysis of sentiment and emotion from user‐generated content has attracted the attention of many research areas and commercial‐marketing domains targeted at studying the social behavior of web users and their public attitudes toward brands, social events, and political actions. Capturing the emotions expressed in the written language could be crucial to support the decision‐making processes: the emotion resulting from a tweet or a review about an item could affect the way to advertise or to trade on the web and then to make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion‐based classification of textual data from a social network by using an extended version of the fuzzy C‐means algorithm called extension of fuzzy C‐means. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results.

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“…Clustering is an unsupervised learning method decomposing a given set of objects into subgroups (i.e. clusters) based on object similarities [ 9]. The objective is to divide the data set in such a way that objects belonging to the same cluster are as similar as possible whereas objects belonging to different clusters are as dissimilar as possible [10].…”

Section: Clusteringmentioning

confidence: 99%

Understanding Customer's Evaluations Through Mining Airline Reviews

Yakut¹,

Turkoglu²,

Yakut³

2015

IJDKP

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Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn customer expectations and requirements. Airline customers have different characteristics and if passenger reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction by improving provided services. In this study, we investigate customer review data for in-flight services of airline companies and draw customer models with respect to such data. In this sense, we apply two approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are grouped into categories based on features such as cabin flown types, experienced airline companies. In clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We apply multivariate regression analysis to model customer groups in both cases. During this, we try to understand how customers evaluate the given services and what dominant characteristics of in-flight services can be from the customer viewpoint.

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Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

Abstract: Clustering is one of the data mining techniques that have been around to discover business intelligence by

Cited by 21 publications

References 12 publications

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures

A lightweight clustering–based approach to discover different emotional shades from social message streams

Understanding Customer's Evaluations Through Mining Airline Reviews

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