Sistem Rekomendasi Film Menggunakan Integrated Kohonen K-Means clustering

Joshua Maximillian, Henry Novianus Palit, Alvin Nathaniel Tjondrowiguno

Abstract


With the development of the film industry, more and more films can be watched. But because there are too many films that can be watched that cause users to be confused in finding films that match what they like. So there is a movie recommendation system to help user. The movie recommendation system itself has various ways to produce movie recommendations that users might like.The movie recommendation system using Integrated Kohonen K-Means Clustering is one of the Data Mining methods that can be used in recommending films. Intergrated Kohonen K-Means Clustering compared to Kohonen Self Organizing Maps, and also K-Means Clustering in recommending films.According to the result of Integrated Kohonen K-Means Clustering to know how many K cluster that is optimal for K-Means Clustering use the Elbow Method. To know how good the cluster you produce use Silhouette Coefficient and the score -0.389 for the Integrated Kohonen K-Means Clustering. The Mean Reciprocal Rank produced by Integrated Kohonen K-Means Clustering which score is 0.362 is better than K-Means Clustering which score is 0.003 and Kohonen Self Organizing Maps which score is 0.002.


Keywords


KSOM; K-Means Clustering; Data Mining; Movie Recommendation; Kohonen K-Means;Elbow Method; Silhouette Coefficient; MRR

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References


Aranganayagi & Thangavel, 2007. Clustering Categorical Data Using Silhouette Coefficient as a Relocating Measure. URI= https://ieeexplore.ieee.org/abstract/document/4426662

Bholowalia & Kumar, 2014. EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN. DOI=10.1.1.735.7337

Caragea, C., Honavar, V., Boncz, P., Boncz, P., Larson, P.-Å., Dietrich, S. W., Wolfson, O., 2009. Mean Reciprocal Rank. URI= https://link.springer.com/referenceworkentry/10.1007%2F978-0-387-39940-9_488

Grouplens, 2019. URI= https://grouplens.org/datasets/movielens/

Kużelewska ,2014. Clustering Algorithms in Hybrid Recommender System on MovieLens Data. URI=http://logika.uwb.edu.pl/studies/download.php?volid=50&artid=50-07&format=PDF

Mishra & Behera ,2012. Kohonen Self Organizing Map with Modified K-means clustering For High Dimensional Data Set. URI= https://pdfs.semanticscholar.org/9ef4/d5d6503c706ba5f09ed18fe5bef2c4ef62f8.pdf

Praba et al.,2018. Movie Recommendation System. URI= https://www.ijresm.com/Vol_1_2018/Vol1_Iss10_October18/IJRESM_V1_I10_217.pdf

Prabhu, 2015. K-mean Clustering Algortihm. URI= https://www.slideshare.net/parryprabhu/k-meanclustering-algorithm

Raphael, 2015. Kohonen self organizing maps. URI=https://www.slideshare.net/raphaelkiminya/kohonen-self-organizing-maps

Seo E. & Choi H., 2010. Movie Recommendation with K-Means Clustering and Self Organizing Methods. URI=https://scitepress.org/papers/2010/27376/27376.pdf

Wadia & Gupta ,n.d.. Movie Recommendation System based on Self-Organizing Maps. URI= http://www.kaivanwadia.com/Projects/MRS-NN.pdf


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