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Movie Popularity Classification based on Inherent Movie Attributes using C4.5, PART and Correlation Coefficient

机译:电影人气基于使用C4.5,零件和相关系数的固有电影属性的分类

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Abundance of movie data across the internet makes it an obvious candidate for machine learning and knowledge discovery. But most researches are directed towards bi-polar classification of movie or generation of a movie recommendation system based on reviews given by viewers on various internet sites. Classification of movie popularity based solely on attributes of a movie i.e. actor, actress, director rating, language, country and budget etc. has been less highlighted due to large number of attributes that are associated with each movie and their differences in dimensions. In this paper, we propose classification scheme of pre-release movie popularity based on inherent attributes using C4.S and PART classifier algorithm and define the relation between attributes of post release movies using correlation coefficient.
机译:互联网上的电影数据丰富使其成为机器学习和知识发现的明显候选者。但大多数研究都针对基于各种互联网网站上观众给出的评论的电影或电影推荐系统的双极分类。由于与每部电影相关的大量属性及其尺寸差异,因此基于电影的电影的属性的电影普及的分类,即演员,女演员,总监,语言,国家和预算等。在本文中,我们基于使用C4.S和零件分类器算法的固有属性提出了预发布电影人气的分类方案,并使用相关系数定义了后发布电影的属性之间的关系。

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