Unsupervised learning through symbolic clustering
Pattern Recognition Letters
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Renyi's divergence and entropy rates for finite alphabet Markov sources
IEEE Transactions on Information Theory
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Detecting anomalous records in categorical datasets
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregate distance based clustering using fibonacci series-FIBCLUS
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Association-Based dissimilarity measures for categorical data: limitation and improvement
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Hi-index | 0.10 |
In this paper, we propose a novel method to measure the dissimilarity of categorical data. The key idea is to consider the dissimilarity between two categorical values of an attribute as a combination of dissimilarities between the conditional probability distributions of other attributes given these two values. Experiments with real data show that our dissimilarity estimation method improves the accuracy of the popular nearest neighbor classifier.