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.

Mining repeating patterns from music data is one of the most interesting issues of multimedia data mining. However, less work are proposed for mining polyphonic repeating patterns. Hence, two efficient algorithms, A-PRPD (Apriori-based Polyphonic Repeating Pattern Discovery) and T-PRPD (Tree-based Polyphonic Repeating Pattern Discovery), are proposed to discover polyphonic repeating patterns from music data. Furthermore, a bit-string method is developed for improving the efficiency of the proposed algorithms. Experimental results show that the proposed algorithms, A-PRPD and T-PRPD, are both effective and efficient methods for mining polyphonic repeating patterns from synthetic music data and real data.

Efficient mining of high utility itemsets has become one of the most interesting data mining tasks with broad applications. In this paper, we proposed two efficient one-pass algorithms, MHUI-BIT and MHUI-TID, for mining high utility itemsets from data streams within a transaction-sensitive sliding window. Two effective representations of item information and an extended lexicographical tree-based summary data structure are developed to improve the efficiency of mining high utility itemsets. Experimental results show that the proposed algorithms outperform than the existing algorithms for mining high utility itemsets from data streams.

Mining frequent melody structures from music data is one of the most important issues in multimedia data mining. In this paper, we proposed an efficient online algorithm, called BVMDS (Bit-Vector based Mining of Data Streams), to mine all frequent temporal patterns over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in BVMDS to reduce the time and memory needed to slide the windows. An effective list structure is used to overcome the performance bottleneck of previous work, FTP-stream. Experiments show that the BVMDS algorithm outperforms FTP-stream algorithm, and just scans the streaming data once.

Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once

In this paper, we propose an efficient algorithm, called MQS-change (changes of music query streams), to detect the changes of maximal melody structures in user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new data structure MSC-list (a list of music structure changes). Experiments show that MQS-change algorithm is an online, single-pass approach to detect the changes of music melody structures over continuous music query streams

Online mining of streaming data is one of the most important issues in data mining. In this paper, we proposed an efficient one-pass algorithm, called MFI-TransSW (mining frequent itemsets over a transaction-sensitive sliding window), to mine the set of all frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. The experiments show that the proposed algorithm not only attain highly accurate mining results, but also run significant faster and consume less memory than existing algorithms for mining frequent itemsets over recent data streams.

In this paper, we proposed an online algorithm, called FQT-Stream (Frequent Query Trees of Streams), to mine the set of all frequent tree patterns over a continuous XML data stream. A new numbering method is proposed to represent the tree structure of a XML query tree. An effective sub-tree numeration approach is developed to extract the essential information from the XML data stream. The extracted information is stored in an effective summary data structure. Frequent query trees are mined from the current summary data structure by a depth-first-search manner.

Online, single-pass mining Web click streams poses some interesting computational issues, such as unbounded length of streaming data, possibly very fast arrival rate and just one scan over previously arrived click-sequencer In this paper, we propose a new, single-pass algorithm, called DSM-TKP (data stream mining for top-k path traversal patterns), for mining top-k path traversal patterns, where k is the desired number of path traversal patterns to be mined. An effective summary data structure called TKP-forest (top-k path forest) is used to maintain the essential information about the top-k path traversal patterns of the click-stream so far. Experimental studies show that DSM-TKP algorithm uses stable memory usage and makes only one pass over the streaming data.

We study the problem of mining frequent closed structures in a continuous, infinite-sized, and fast changing music melody stream. By modeling a music melody as a sequence of chord-sets, we propose an efficient algorithm FCS-stream (frequent closed structures of streaming melody sequences) for incremental mining of frequent closed structures in one scan of the continuous stream of chord-set sequences. An extended prefix-tree structure called TCS-tree (temporal closed structure tree) is developed for storing compact, essential information about the frequent closed structures of the stream. Results from our theoretical analysis and experimental studies with synthetic data show that the FCS-stream algorithm satisfies the main performance requirements, namely, single-pass, bounded memory, and real-time, for data stream mining.