@inproceedings{bc9dcbcbba7742398a2e440369b6bf6f,
title = "A new cell-based clustering method for high-dimensional data mining applications",
abstract = "Many clustering methods are not suitable for high-dimensional data mining applications because of the so-called 'curse of dimensionality' and the limitation of available memory. In this paper, we propose a new cell-based clustering method for the high-dimensional data mining applications. The proposed clustering method provides efficient cell creation and cell insertion algorithms using a space-partitioning technique, as well as makes use of a filtering-based index structure using an approximation technique. In addition, we compare the performance of our cell-based clustering method with the CLIQUE method which is well known as an efficient grid-based clustering method for high-dimensional data. The experimental results show that our clustering method achieves better performance on cluster construction time and retrieval time.",
keywords = "Cell-based clustering methods, High-dimensional data mining",
author = "Chang, \{Jae Woo\}",
year = "2005",
doi = "10.1007/11552413\_56",
language = "English",
isbn = "3540288945",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "391--397",
booktitle = "Knowledge-Based Intelligent Information and Engineering Systems - 9th International Conference, KES 2005, Proceedings",
note = "9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005 ; Conference date: 14-09-2005 Through 16-09-2005",
}