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Full-Text Articles in Computer Sciences
Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu
Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu
David LO
With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose Spider- Mine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1 − ϵ. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call “spiders”. With the spider structure, our approach adopts a …
Data Mining For Software Engineering, Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu
Data Mining For Software Engineering, Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu
David LO
To improve software productivity and quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks. However, mining SE data poses several challenges. The authors present various algorithms to effectively mine sequences, graphs, and text from such data.
Benchmarking Multimedia Databases, Arcot Desai Narasimhalu, Mohan S. Kankanhalli, Kang Wu Jian
Benchmarking Multimedia Databases, Arcot Desai Narasimhalu, Mohan S. Kankanhalli, Kang Wu Jian
Arcot Desai NARASIMHALU
Multimedia technologies are being adopted both in the professional and commercial world with great enthusiasm. This has led to a significant interest in the research and development of multimedia databases. However, none of these efforts have really addressed the issues related to the benchmarking of multimedia databases. We analyze the problem of benchmarking multimedia databases in this paper and suggest a methodology
Colour Matching For Imaging Retrieval, Babu M. Mehtre, Mohan S. Kankanhalli, Arcot Desai Narasimhalu, Guo Chang Man
Colour Matching For Imaging Retrieval, Babu M. Mehtre, Mohan S. Kankanhalli, Arcot Desai Narasimhalu, Guo Chang Man
Arcot Desai NARASIMHALU
Color is an important attribute for image matching and retrieval. We present two new color matching methods, the Reference Color Table Method and a Distance Method, for image retrieval. Both these methods and an existing method Histogram Intersection were implemented and tested for a database size of 170 color images. To compare the efficacy of each method, a figure of merit, called Efficiency of Retrieval, is defined. The results show that both the new methods perform better than the existing method, and that the Reference Color Table Method gives the best results.
Fuzzy Content-Based Retrieval In Image Databases, Kang Wu Jian, Arcot Desai Narasimhalu
Fuzzy Content-Based Retrieval In Image Databases, Kang Wu Jian, Arcot Desai Narasimhalu
Arcot Desai NARASIMHALU
Image data are inherently visual. The description of visual characteristics of images are imprecise. Fuzzy retrieval of images stored in a feature-based image database is a natural means to access the data. Unfortunately, to the authors knowledge, little work has been done on fuzzy image database models and fuzzy retrieval of feature-based image databases. In this paper, a fuzzy image database model and a concept of fuzzy space are proposed and fuzzy query processing in fuzzy space and fuzzy indexing on complex fuzzy vectors are described. An example image database, the computer-aided facial image inference and retrieval system (CAFIIR), is …