## Abstract

This paper proposes a grid-based clustering method that dynamically partitions the range of a grid-cell based on its distribution statistics of data elements in a data stream. Initially the multi-dimensional space of a data domain is partitioned into a set of mutually exclusive equal-size initial cells. As a new data element is generated continuously, each cell monitors the distribution statistics of data elements within its range. When the support of data elements in a cell becomes high enough, the cell is dynamically divided into two mutually exclusive smaller cells called intermediate cells by assuming the distribution of data elements is a normal distribution. Eventually, the dense sub-range of an initial cell is recursively partitioned until it becomes the smallest cell called a unit cell. In order to minimize the number of cells, a sparse intermediate or unit cell can be pruned if its support becomes much less than a minimum support. The performance of the proposed method is comparatively analyzed through a series of experiments.

Original language | English |
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Pages (from-to) | 387-398 |

Number of pages | 12 |

Journal | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) |

Volume | 2838 |

DOIs | |

Publication status | Published - 2003 |

Event | 7th European Conference on Principles and Practice of Knowledge Discovery in Databases - Cavtat-Dubrovnik, Croatia Duration: 2003 Sep 22 → 2003 Sep 26 |

## All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)