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Chapter 5 Exploitation of Multivalued Type Proximity for Symbolic Feature Selection 5 . 0
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2016 |
| Abstract | In this chapter, an important aspect of cluster analysis called curse of dimensionality is addressed, particularly for symbolic objects described by both interval and multivalued features. Proximity measures and clustering algorithms are computationally expensive if the niunber of features describing symbolic objects is very large. The length of featiu-e vectors representing symbolic objects depends upon the number of features recorded and larger the length of the feature vectors, higher is the dimension of the feature space. Thus, besides creating analysis problem, it creates an added problem requiring large memory for storing symbolic objects. Moreover, it |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://shodhganga.inflibnet.ac.in:8080/jspui/bitstream/10603/92269/12/12_chapter%205.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
