Adaptive Density-Based Spatial Clustering for Massive Data Analysis

Adaptive Grids for Clustering Massive Data Sets

Clustering is a key data mining problem. Density and grid based technique is a popular way to mine clusters in a large multi-dimensional space wherein clusters are regarded as dense regions than their surroundings. The attribute values and ranges of these attributes characterize the clusters. Fine grid sizes lead to a huge amount of computation while coarse grid sizes result in loss in quality ...

Generalized density based clustering for spatial data mining

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

A Review of Density-Based clustering in Spatial Data

Data mining is a non-trivial process. That is identifying novel, valid and potentially useful patterns in data. Data mining supports automatic data exploration. That is extracting hidden information from the huge database. Data mining refers to search useful and relevant information from the database. Spatial mining is a branch of data mining. The spatial mining deals with the location or geo-r...

A novel spatial clustering method based on wavelet network and density analysis for data stream

With the limited memory and time, a fast and effective clustering can’t be achieved for massive, highspeed data stream, so this paper mainly studies the key method of data stream clustering under the restriction of resource, and then proposes a dynamic data stream clustering algorithm (D-DStream) based on wavelet network and density, which uses sliding window to process data stream. Firstly, ap...