Clustering method based on fuzzy equivalence relation

Raut, Anjali B.; Bamnote, G. R.

doi:10.1109/iccct.2011.6075209

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Eghbal [9] has introduced a fuzzy rule-based clustering algorithm, which explores the potential clusters in the data patterns automatically to identify some interpretable fuzzy rules. Raut et al proposed a web fuzzy clustering method to be implemented in web user clustering and web page clustering in web usage mining [10]. The web users can be generally classified based on the correlative degree of the web user as firm relation users, hypo-firm relation users, hypo-infirm relation users and infirm relation users.…”

Section: Related Work On Predicting User's Behavioral Patternmentioning

confidence: 99%

Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining

Serin¹,

Serin²,

SatheeshKumar³

2022

Journal of Internet Technology

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<p>Online business and marketing are becoming popular now a day due to the wide variety of products available from multiple vendors in online. One of the major challenges of e-business merchants is predicting the buying and selling patterns of online customers. Global level competition is another challenge faced by online merchants due to the lowest prices and offers provided by multiple sellers for the same or similar product. Hence, the development of an efficient web mining framework to analyze and predict buyer’s interest based on the browsing history will be a great support to the online sellers by providing exact or relevant product details to the buyers in online. Association rule mining plays an essential role in Web Mining for finding the most frequent and predictive patterns of the user. The major challenge in this approach is the generation of many rules for a huge volume of datasets. Decision making based on association rule mining is critical because knowledge is not directly present in frequent patterns. This research work focuses on the analysis of standard web mining approaches such as k-means clustering, fuzzy c-means clustering, fuzzy k-medoids clustering and fuzzy clustering with weighted session page matrix approach. In this work, MSNBC dataset from UCI Machine Learning Repository has been taken for analysis. Dimensionality reduction plays an important role in the accurate classification of users with respect to their interests. This research work proposed fuzzy C-Means using Kernel Principal Component Analysis (k-PCA) as a dimensionality reduction method based association rule mining classification, grouping and pattern prediction with 100% “confidence” along with a “lift” value greater than 1. The “support” value also shows higher compare with other existing methods and features are effectively reduced in the proposed architecture.</p> <p> </p>

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Section: Related Work On Predicting User's Behavioral Patternmentioning

confidence: 99%

Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining

Serin¹,

Serin²,

SatheeshKumar³

2022

Journal of Internet Technology

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“…Finding the relevant information on www is not an easy task. Clustering is one of the Data Mining techniques to improve the efficiency in information finding process [4]. Document clustering has become an increasingly important technique for enhancing search engine results, web crawling, unsupervised document organization, and information retrieval or filtering [5].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Document Clustering via Optimal Centroid Selection using Adaptive Pillar K-means – Gaussian Firefly Algorithm

Nagalashmi¹

2019

Journal of Intelligent Computing

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In recent days, data growth is enormous and tough to handle it, in an oragnised manner and in apt time. In data mining, wide range of researches are available for managing data effectively. Document clustering is a spirited zone in data mining and here our main objective is to assemble the related documents. In this paper, we generate an algorithm for clustering by means of Adaptive Pillar K-Means and Gaussian Firefly Algorithm. For determining the proper centroid in order to attain the proper clustered documents, Adaptive Pillar K-means algorithm is utilized. Subsequently, Gaussian firefly algorithm is exploited for the optimization process and also for enhancing the precision that results in reducing the sum of squared errors and computational time. Here, the performance of the proposed methodology is compared with various algorithms such as Genetic Algorithm, Ant colony optimization and gravity clustering. The attained results show the performance of the proposed methodology and the simulation results illustrated the betterment in quality with low sum of squared errors.

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“…The FEC algorithm has been applied in a variety of fields, such as information retrieval, knowledge discovery, data mining, etc. [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems

Liu

2016

Knowledge-Based Systems

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In order to deal with the fuzzy large-scale multiple-criteria group decision-making (FLMCGDM) problems, this paper incorporates clustering analysis and information aggregation operator into the problems of large-scale multiple-criteria group decision-making with interval type-2 fuzzy sets (IT2 FSs). The interval type-2 fuzzy equivalence clustering (IT2-FEC) analysis is used to classify decision-makers (DMs) to reduce the dimension of the large-scale DMs in the FLMCGDM problems. The combined weighted geometric averaging (CWGA) operator is extended into the case with IT2 FSs variables, which can take both the importance of individual and its relative position into account. Afterwards, a solution process for the FLMCGDM problems is proposed, in which the new equivalence clustering method and CWGA operator of IT2 FSs is incorporated. Finally, the reasonability and effectiveness of the proposed method are verified by an illustrative example. Compared with other methods, the IT2-FEC analysis can deal with the linguistic variables and produce dynamic clustering results in a more efficient way.

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Clustering method based on fuzzy equivalence relation

Cited by 7 publications

References 11 publications

Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining

Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining

Hierarchical Document Clustering via Optimal Centroid Selection using Adaptive Pillar K-means – Gaussian Firefly Algorithm

An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems

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