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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Aryal, Sunil Ting, Kai Ming Haffari, Gholamreza Washio, Takashi |
| Copyright Year | 2014 |
| Abstract | Nearest neighbour search is a core process in many data mining algorithms. Finding reliable closest matches of a query in a high dimensional space is still a challenging task. This is because the effectiveness of many dissimilarity measures, that are based on a geometric model, such as lp-norm, decreases as the number of dimensions increases. In this paper, we examine how the data distribution can be exploited to measure dissimilarity between two instances and propose a new data dependent dissimilarity measure called 'mp-dissimilarity'. Rather than relying on geometric distance, it measures the dissimilarity between two instances in each dimension as a probability mass in a region that encloses the two instances. It deems the two instances in a sparse region to be more similar than two instances in a dense region, though these two pairs of instances have the same geometric distance. Our empirical results show that the proposed dissimilarity measure indeed provides a reliable nearest neighbour search in high dimensional spaces, particularly in sparse data. Mp-dissimilarity produced better task specific performance than lp-norm and cosine distance in classification and information retrieval tasks. |
| Starting Page | 707 |
| Ending Page | 712 |
| File Size | 277882 |
| Page Count | 6 |
| File Format | |
| ISSN | 15504786 |
| e-ISBN | 9781479943029 |
| DOI | 10.1109/ICDM.2014.33 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-12-14 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Accuracy Information retrieval Vectors Educational institutions Approximation methods Data mining Electronic mail mp-dissimilarity distance measure lp-norm |
| Content Type | Text |
| Resource Type | Article |
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