Stones Lei Zhang
P. R. China
Email: firstname.lastname@example.org /
email@example.com (large attached file)
Stones is currently a PhD Candidate in School of Computer Science and Engineering, at University of Electronic Science and Technology of China. His advisor is Prof. Zhang Yi.
Stones received the Master and Bachelor degree in School of Computer Science and Engineering, at University of Electronic Science and Technology of China in 2006 and 2001. He has worked in the EASYCON and E-TOYOU as software engineer or project manager for Core Banking System Software of Commercial Bank from 2001 to 2003.
Stones’ research interests include data mining, neural network, machine learning, Computational Intelligence etc.
My main research interests are in the fields of knowledge discovery, especially on the high dimension data sets and multimedia data. Related topics include data mining, information retrieval, neural networks, machine learning, pattern recognition... I'd like to help people get what they need more easily. Let the computer do more for us with less help from us, learn from experience and data, adapt effortlessly, and discover new knowledge. We need computers that reduce the information overload by extracting the important patterns from masses of data. And we need computer understand what we need. This poses many deep and fascinating scientific problems: How can a computer decide autonomously which representation is best for target knowledge? How can it tell genuine regularities from chance occurrences? How can pre-existing knowledge be exploited? How can learned results be made understandable by us?
My research addresses these and related questions. Research topics that I'm working, include:
1. Subspace clustering in high dimension database
Subspace clustering aims at computing all clusters in all subspaces of the feature space. The information of objects clustered differently in varying subspaces is conserved. Objects may be assigned to several clusters (in different subspaces).
2. project clustering in high dimension database
Projected clustering aims at partitioning the data into a set of clusters and a noise set while allowing the clusters to exist in different subspaces. Thus, for each cluster, a different set of features is relevant, whith the remaining features being irrelevant. The points of each cluster are densely packed in the subspace spanned by the corresponding relevant features but are sparsely distributed in the subspace spanned by the other dimensions.
3. Clustering Categorical Data Using Coverage Density
The coverage density is defined as the percentage of the occupied area of all data points to the whole rectangle area decided by the distinct attribute values and the number of data points. The idea of coverage density is proved simple but feasible. The simple idea makes the algorithm fast, memory and time saving.
4. PCASOM neural networks and Competitive Learning for clustering
5. Online and distribute data mining
6. Multimedia data mining
Last modified: June 21 2006