Abstract: Aiming at the classification problem of large-scale document information, a large-scale data clustering algorithm based on improved CURE algorithm is proposed. By clustering the data ...

There are many clustering algorithms proposed over time ... some are limited to specific types of data distributions. This paper aims to demonstrate the importance of Cure algorithm over the K-Means ...

the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of ...

CURE Algorithm Overview The CURE (Clustering Using Representatives) algorithm is a hierarchical clustering method designed to efficiently cluster large datasets by iteratively merging data points into ...

In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. Within the last several years, many clustering algorithms have been proposed in this area of ...

Researched and worked on four clustering algorithms: K-modes, ROCK, BIRCH and CURE. Analyzed each of these algorithms, and displayed a comparison between them, through practical implementation.

Clustering algorithms are powerful tools for data analysis, as they can help you discover hidden patterns and groups in your data. Clustering is a type of unsupervised learning, which means that ...

ABSTRACT: Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining ...