Recommendation systems are refining mechanism to envisage the ratings for items and users, to recommend likes mainly from the big data. Our proposed recommendation system gives a mechanism to users to classify with the same interest. This recommender system becomes core to recommend the e-commerce and various websites applications based on similar likes. This central idea of our work is to develop movie recommender system with the help of clustering using K-means clustering technique and data pre-processing using Principal Component Analysis (PCA). In this proposed work, new recommendation technique has been presented using K-means clustering, PCA and sampling with the help of MovieLens dataset. Our proposed method and its subsequent results have been discussed and collation with other existing methods using evaluation metrics like Dunn Index, average similarity and computational time has been also explained and prove that our technique is best among other techniques. The results achieve from the MovieLens dataset is able to prove high efficiency and accuracy of our proposed work. Our proposed method is able to achieve the MAE of 0.67, which is better than other methods.

中文翻译：

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K-means聚类和PCA的电影推荐系统的新方法

推荐系统是一种细化机制，用于设想商品和用户的评级，主要从大数据中推荐喜欢的商品。我们提出的推荐系统为用户提供了一种兴趣相同的分类机制。该推荐器系统成为基于相似点推荐电子商务和各种网站应用程序的核心。我们工作的中心思想是借助K-means聚类技术进行聚类并使用主成分分析（PCA）进行数据预处理，从而开发电影推荐系统。在这项拟议的工作中，已经提出了使用K-means聚类，PCA和借助MovieLens数据集进行采样的新推荐技术。我们讨论了我们提出的方法及其后续结果，并使用诸如Dunn Index，平均相似度和计算时间也得到了解释，并证明我们的技术是其他技术中最好的。从MovieLens数据集获得的结果能够证明我们提出的工作的高效率和准确性。我们提出的方法能够实现0.67的MAE，这比其他方法要好。