Time series clustering based on sparse subspace clustering algorithm and its application to daily box-office data analysis

Abstract

Movie box-office research is an important work for the rapid development of the film industry, and it is also a challenging task. Our study focuses on finding the regular box-office revenue patterns. Clustering algorithm is unsupervised machine learning algorithm which classifies the data in the absence of early knowledge of the classes. Unlike static data, the time series data vary with time. The work focused on time series clustering analysis is relatively less than those focused on static data. In this paper, the sparse subspace clustering (SSC) algorithm is introduced to analyze the time series data. The SSC algorithm has a better performance both on the artificial data set and the daily box-office data than recently developed well-known clustering algorithm such as K-means and spectral clustering algorithm. On the artificial data set, SSC is more suitable for time series, whether from the angle of clustering error or visualization. On the actual data, movies are divided into five clusters by SSC algorithm, and each cluster represents a distinct type of distribution pattern. And these patterns can be used in movie recommendation, film evaluation and can guide theater exhibitors and distributors. In addition, this is the first time to apply SSC to deal with time series clustering problem and get a pleasant effect.