Heidi matrix: nearest neighbor driven high dimensional data visualization
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Local and global intrinsic dimensionality estimation for better chemical space representation
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
Clustering algorithm based on mutual K-nearest neighbor relationships
Statistical Analysis and Data Mining
Enhancing density-based clustering: Parameter reduction and outlier detection
Information Systems
A graph-based topic extraction method enabling simple interactive customization
Proceedings of the 2013 ACM symposium on Document engineering
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In this paper, we use a simple concept based on k-reverse nearest neighbor digraphs, to develop a framework RECORD for clustering and outlier detection. We developed three algorithms - (i) RECORD algorithm (requires one parameter), (ii) Agglomerative RECORD algorithm (no parameters required) and (iii) Stability-based RECORD algorithm( no parameters required). Our experimental results with published datasets, synthetic and real-life datasets show that RECORD not only handles noisy data, but also identifies the relevant clusters. Our results are as good as (if not better than) the results got from other algorithms.