DISCOVERING EMERGING MELODY PATTERNS FROM CUSTOMER QUERY DATA STREAMS OF MUSIC SERVICE

Abstract

Mining melody structure patterns from music query data is one of the most interesting issues of multimedia data mining. In this paper, we introduce a new kind of melody structure pattern, called emerging melody pattern (EMP), for knowledge discovery from music query data streams. EMPs are defined as music data items with melody item-sets whose support increase significantly from one sliding window to another window from streaming melody data sequences. An efficient data mining algorithm, called MEMPA (Mining Emerging Melody Pattern Algorithm), is proposed to discover all EMPs from music query data. In the framework of MEMPA, a prefix tree-based structure, called EMP-tree (Emerging Melody Pattern tree), is developed for storing EMPs effectively from current stream sliding windows. Experimental results show that the proposed algorithm is an efficient method for discovering all EMPs from streaming melody sequences.