Neighboring feature clustering

  • Authors:

  • Zhifeng Wang;Wei Zheng;Yuhang Wang;James Ford;Fillia Makedon;Justin D. Pearlman

  • Affiliations:

  • Dept. of Computer Science, Dartmouth College, Hanover, NH;Dept. of Computer Science, Dartmouth College, Hanover, NH;Dept. of Computer Science and Engineering, Southern Methodist University, Dallas, TX;Dept. of Computer Science, Dartmouth College, Hanover, NH;Dept. of Computer Science, Dartmouth College, Hanover, NH;Dept. of Computer Science, Dartmouth College, Hanover, NH

  • Venue:
  • SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
  • Year:
  • 2006

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Abstract

In spectral datasets, such as those consisting of MR spectral data derived from MS lesions, neighboring features tend to be highly correlated, suggesting the data lie on some low-dimensional space. Naturally, finding such low-dimensional space is of interest. Based on this real-life problem, this paper extracts an abstract problem, neighboring feature clustering (NFC). Noticeably different from traditional clustering schemes where the order of features doesn't matter, NFC requires that a cluster consist of neighboring features, that is features that are adjacent in the original feature ordering. NFC is then reduced to a piece-wise linear approximation problem. We use minimum description length (MDL) method to solve this reduced problem. The algorithm we proposed works well on synthetic datasets. NFC is an abstract problem. With minor changes, it can be applied to other fields where the problem of finding piece-wise neighboring groupings in a set of unlabeled data arises.