A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach

Selvi, C. Thirumarai; Sivasankar, E.

doi:10.1007/s00500-017-2899-6

Cited by 30 publications

(12 citation statements)

References 46 publications

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“…Here, the cluster was efficient, but suffers from higher computational cost. In addition, a Modified Cuckoo Search (MCS) algorithm and a Modified Fuzzy C Means (MFCM) approach was developed by Selvi et al [12]. In this method, the number of iterations and error rates were reduced by MFCM.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The performance of the proposed approach is evaluated against the known measure for prediction and recommendations and is given below: For prediction, MAE is used and represented as the difference between the predicted rating of user u on item i(p u,i ) and the actual rating of user u on item i(r u,i ) and is represented in Eq. (12). For recommendation, precision and recall, and f-measures are used.…”

Section: Performance Measurementioning

confidence: 99%

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Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Parthasarathy¹,

Devi²

2022

Computers, Materials &Amp; Continua

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Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Performance Measurementioning

confidence: 99%

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Parthasarathy¹,

Devi²

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Selvi et al [27] proposed a new RS that originates the Collaborative filtering approach in "A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach". The rating users have been clustered with minimal error rate using a proposed modified fuzzy c-means (MFCM) clustering approach.…”

Section: Web Page Recommender Systemsmentioning

confidence: 99%

A novel web page recommender using data automatic clustering and Markov process

Monfared

2019

SN Appl. Sci.

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As the web expands and data grow in the networks, finding useful knowledge from the World Wide Web becomes a major challenge. As intelligent systems, the recommender systems help users to find their favorite resource among a large body of information. This research aimed at providing a new page recommender system, which enhances the accuracy of the suggested pages based on the user interest, Automatic Clustering-based Genetic Algorithm (ACGA), and all modified 3th-order Markov Model. Therefore, the frequency and page visiting time by each user have been extracted from the log file of the web server, while the user interest in the page during a session has been computed by a linear combination. ACGA involved 2 phases of the cluster based on (1) similarity measure and (2) Genetic Algorithm. The novel cluster partitioned the vectorization sessions in separate clusters through a new fitness function. Moreover, all modified 3th-order Markov Model used all orders of Markov Model simultaneously, and predicted the next page in the page recommender system. The research did these experiments on a real CTI dataset. Experimental results and their comparison with other approaches showed superiority in the accuracy of the proposed system.

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“…Over the past decades, clustering analysis has been serving many applications in the field of machine learning [8][9][10], pattern segmentation [11][12][13][14], and recommendation [15][16][17]. Up to now, different kinds of clustering schemes have been developed, density-based clustering methods [18,19], partition-based clustering methods [20][21][22], hierarchical-based clustering methods [23,24], grid-based clustering methods [25,26], model-based clustering methods [27][28][29], graph-based clustering methods [30,31]and others.…”

Section: Introductionmentioning

confidence: 99%

Density peaks clustering algorithm with connected local density and punished relative distance

Xiong,

Zang,

Zhao

et al. 2023

J Supercomput

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Density peaks clustering (DPC) algorithm has been widely applied in many fields due to its innovation and efficiency. However, the original DPC algorithm and many of its variants choose Euclidean distance as local density and relative distance estimations, which affects the clustering performance on some specific shaped datasets, such as manifold datasets. To address the above-mentioned issue, we propose a density peak clustering algorithm with connected local density and punished relative distance (DPC-CLD-PRD). Specifically, the proposed approach computes the distance matrix between data pairs using the flexible connectivity distance metric. Then, it calculates the connected local density of each data point via combining the flexible connectivity distance measure and k-nearest neighbor method. Finally, the punished relative distance of each data point is obtained by introducing a connectivity estimation strategy into the distance optimization process. Experiments on synthetic, real-world, and image datasets have demonstrated the effectiveness of the algorithm in this paper.

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A novel optimization algorithm for recommender system using modified fuzzy c-means clustering approach

Cited by 30 publications

References 46 publications

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

A novel web page recommender using data automatic clustering and Markov process

Density peaks clustering algorithm with connected local density and punished relative distance

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