Nearest neighbour distance matrix classification

Mohd Shamrie Sainin and Rayner Alfred (2010) Nearest neighbour distance matrix classification. In: 6th International Conference on Advanced Data Mining and Applications (ADMA 2010), 19-21 November 2010, Chongqing, China.

Full text not available from this repository.


A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures). In this paper, we propose a modified nearest neighbour method called Nearest Neighbour Distance Matrix (NNDM) for classification based on unsupervised and supervised distance matrix. In the proposed NNDM method, an Euclidean distance method coupled with a distance loss function is used to create a distance matrix. In our approach, distances of each instance to the rest of the training instances data will be used to create the training distance matrix (TADM). Then, the TADM will be used to classify a new instance. In supervised NNDM, two instances that belong to different classes will be pushed apart from each other. This is to ensure that the instances that are located next to each other belong to the same class. Based on the experimental results, we found that the trained distance matrix yields reasonable performance in classification. © 2010 Springer-Verlag.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: Classification, Data mining, Distance matrix, Machine learning, Nearest neighbour
Subjects: Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: SCHOOL > School of Engineering and Information Technology
Depositing User: ADMIN ADMIN
Date Deposited: 10 Mar 2011 14:41
Last Modified: 29 Dec 2014 16:16

Actions (login required)

View Item View Item