Viswanath, P and Murty, Narasimha M (2002) An efficient incremental mining algorithm for compact realization of prototypes. UNSPECIFIED.
There are two phases in pattern classi viz design phase (abstractions are created/learnt), classi phase (abstractions are used to classify a test pattern). Classi based on neural networks and genetic algorithms needs more design time. Where as nearest neighbor classi have zero design time, they need more classi time. PC-tree (pattern count tree) based classifiers trikeskes a balance between the above mentioned two categories. PC-tree is a data structure originally used in incremental data mining to store the database. It can also be used in pattern classi for compact realization of prototypes. PC-Tree based classi has advantages over con- ventional classi nearest neighbor, k-nearest neighbor classi with respect to storage space, classi time, and classi accuracy(CA). In this paper we propose a classi algorithm which is superior to PC-tree based classi in every aspect. This classi is based on a new data structure called partitioned pattern count tree (PPC-tree). PPC-tree is more compact than PC-tree. This results in reduction of space required and classi time. The e number of training patterns stored in PPC -tree is in orders more than the actual number of training patterns, this results in increase of the CA.
|Item Type:||Departmental Technical Report|
|Keywords:||PC Tree;PPC Tree|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||23 Jan 2007|
|Last Modified:||19 Sep 2010 04:12|
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