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Mining inter-transaction association rules is one of the most interesting issues in data mining research. However, in a data stream environment the previous approaches are unable to find the result of the new-incoming data and the original database without recomputing the whole database. In this paper, we propose an incremental mining algorithm, called DSM-CITI (Data Stream Mining for Closed Inter-Transaction Itemsets), for discovering the set of all frequent inter-transaction itemsets from data streams. In the framework of DSM-CITI, a new in-memory summary data structure, ITP-tree, is developed to maintain frequent inter-transaction itemsets. Moreover, algorithm DSM-CITI is able to construct ITP-tree incrementally and uses the property to avoid unnecessary updates. Experimental studies show that the proposed algorithm is efficient and scalable for mining frequent inter-transaction itemsets over stream sliding windows.

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Score reduction is a process that arranges music for a target instrument by reducing original music. In this study we present a music arrangement framework that uses score reduction to automatically arrange music for a target instrument. The original music is first analyzed to determine the type of arrangement element of each section, then the phrases are identified and each is assigned a utility according to its type of arrangement element. For a set of utility-assigned phrases, we transform the music arrangement into an optimization problem and propose a phrase selection algorithm. The music is arranged by selecting appropriate phrases satisfying the playability constraints of a target instrument. Using the proposed framework, we implement a music arrangement system for the piano. An approach similar to Turing test is used to evaluate the quality of the music arranged by our system. The experiment results show that our system is able to create viable music for the piano.

Piano reduction is a process that arranges music for the piano by reducing the original music into the most basic components. In this study we present an automatic arrangement system for piano reduction that arranges music algorithmically for the piano while considering various roles of the piano in music. We achieve this by first analyzing the original music in order to determine the type of arrangement element performed by an instrument. Then each phrase is identified and is associated with a weighted importance value. At last, a phrase selection algorithm is proposed to select phrases with maximum importance to arrangement under the constraint of piano playability. Our experiments demonstrate that the proposed system has the ability to create piano arrangement.