Man-Kwan Shan

Team Formation for Generalized Tasks in Expertise Social Networks

Given an expertise social network and a task consisting of a set of required skills, the team formation problem aims at finding a team of experts who not only satisfy the requirements of the given task but also communicate to one another in an effective manner. To solve this problem, Lappas et al. has proposed the Enhance Steiner algorithm. In this work, we generalize this problem by associating each required skill with a specific number of experts. We propose three approaches to form an effective team for the generalized task. First, we extend the Enhanced-Steiner algorithm to a generalized version for generalized tasks. Second, we devise a density-based measure to improve the effectiveness of the team. Third, we present a novel grouping-based method that condenses the expertise information to a group graph according to required skills. This group graph not only drastically reduces the search space but also avoid redundant communication costs and irrelevant individuals when compiling team members. Experimental results on the DBLP dataset show the teams found by our methods performs well in both effectiveness and efficiency.

Online mining (recently) maximal frequent itemsets over data streams

A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set of all maximal frequent itemsets in landmark windows over data streams. A new summary data structure called summary frequent itemset forest (abbreviated as SFI-forest) is developed for incremental maintaining the essential information about maximal frequent itemsets embedded in the stream so far. Theoretical analysis and experimental studies show that the proposed algorithm is efficient and scalable for mining the set of all maximal frequent itemsets over the entire history of the data streams.

ACCESS METHODS OF IMAGE DATABASE

The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve images in an image database system. The data structure called 2-D string, proposed by Chang et al., is adopted to represent the symbolic pictures. When there are a large number of images in the image database and each image contains many objects, the processing time for image retrievals is tremendous. It is essential to develop efficient access methods for these retrievals. In this paper, the efficient methods for retrieval by objects, retrieval by pairwise spatial relationships and retrieval by subpicture are proposed. All the methods are based on the superimposed coding technique.

Music style mining and classification by melody

Music style is one of the features that people used to classify music. Discovery of music style is helpful for the design of a content-based music retrieval system. In this paper we investigate the mining and classification of music style by melody from a collection of MIDI music. We extract the chord from the melody and investigate the representation of extracted features and corresponding mining techniques for music classification. Experimental results show that the classification accuracy is about 70% to 84% for 2-way classification.

DSM-FI: an efficient algorithm for mining frequent itemsets in data streams

Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions. According to the proposed algorithm, each transaction of the stream is projected into a set of sub-transactions, and these sub-transactions are inserted into a new in-memory summary data structure, called SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far. Finally, the set of all frequent itemsets is determined from the current SFI-forest. Theoretical analysis and experimental studies show that the proposed DSM-FI algorithm uses stable memory, makes only one pass over an online transactional data stream, and outperforms the existing algorithms of one-pass mining of frequent itemsets.

Emotion-based impressionism slideshow with automatic music accompaniment

In this paper, we propose the emotion-based Impressionism slideshow system with automatic music accompaniment. While conventional image slideshow systems accompany images with music manually, our proposed approach explores the affective content of painting to automatically recommend music based on emotions. This is achieved by association discovery between painting features and emotions, and between emotions and music features respectively. To generate more harmonic Impressionism presentation, a linear arrangement method is proposed based on modified traveling salesman algorithm. Moreover, some animation effects and synchronization issues for affective content of Impressionism fine arts are considered. Experimental result shows our emotion-based accompaniment brings better browsing experience of aesthetics.

A personalized music filtering system based on melody style classification

With the growth of digital music, the personalized music filtering system is helpful for users. Melody style is one of the music features to represent users music preference. We present a personalized content-based music filtering system to support music recommendation based on users preference of melody style. We propose the multitype melody style classification approach to recommend the music objects. The system learns the user preference by mining the melody patterns from the music access behavior of the user. A two-way melody preference classifier is therefore constructed for each user. Music recommendation is made through this melody preference classifier. Performance evaluation shows that the filtering effect of the proposed approach meets users preference.

Efficient Maintenance and Mining of Frequent Itemsets over Online Data Streams with a Sliding Window

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.

DSM-PLW: single-pass mining of path traversal patterns over streaming web click-sequences

Mining Web click streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some interesting characteristics, such as unknown or unbounded length, possibly a very fast arrival rate, inability to backtrack over previously arrived click-sequences, and a lack of system control over the order in which the data arrive. In this paper, we propose a projection-based, single-pass algorithm, called DSM-PLW (Data Stream Mining for Path traversal patterns in a Landmark Window), for online incremental mining of path traversal patterns over a continuous stream of maximal forward references generated at a rapid rate. According to the algorithm, each maximal forward reference of the stream is projected into a set of reference-suffix maximal forward references, and these reference-suffix maximal forward references are inserted into a new in-memory summary data structure, called SP-forest (Summary Path traversal pattern forest), which is an extended prefix tree-based data structure for storing essential information about frequent reference sequences of the stream so far. The set of all maximal reference sequences is determined from the SP-forest by a depth-first-search mechanism, called MRS-mining (Maximal Reference Sequence mining). Theoretical analysis and experimental studies show that the proposed algorithm has gently growing memory requirements and makes only one pass over the streaming data.

Content-based video retrieval via motion trajectories

Motion is one of the most prominent features of video. For content-based video retrieval, motion trajectory is the intuitive specification of motion features. In this paper, approaches for video retrieval via single motion trajectory and multiple motion trajectories are addressed. For the retrieval via single motion trajectory, the trajectory is modeled as a sequence of segments and each segment is represented as the slope. Two quantitative similarity measures and corresponding algorithms based on the sequence similarity are presented. For the retrieval via multiple motion trajectories, the trajectories of the video are modeled as a sequence of symbolic pictures. Four quantitative similarity measures and algorithms, which are also based on the sequence similarity, are proposed. All the proposed algorithms are developed based on the dynamic programming approach.