Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Issues in data stream management
ACM SIGMOD Record
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
ACM SIGMOD Record
Online mining maximal frequent structures in continuous landmark melody streams
Pattern Recognition Letters
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
Discovering nontrivial repeating patterns in music data
IEEE Transactions on Multimedia
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Online mining of frequent patterns from music data is one of the most important research issues of multimedia data mining. Most previous studies require the specification of a min_support threshold and aim at mining a complete set of frequent patterns satisfying min_support. However, in practice, it is difficult for users to provide an appropriate value of min_support threshold. In this paper, we propose a new problem of multimedia data mining: online mining of top-k melody structures of length no less than min_l, where k is the desired number of hot melody structures to be mined and min_l is the minimal length of each melody structure. An efficient single-pass algorithm, called top-k-HMS (top-k Hot Melody Structures), is developed for mining such melody structures without min_support. In the framework of top-k-HMS algorithm, a new summary data structure, called TKM-list (top-k melody list) is developed to maintain the essential information about the top-k hot melody structures from the current melody sequence streams. Experimental studies show that the proposed top-k-HMS algorithm is an efficient one-pass method for mining the set of top-k Hot Melody Structures over a continuous stream of melody sequences.