Robust Distance-Based Clustering with Applications to Spatial Data Mining

CUSTOMER CLUSTERING BASED ON FACTORS OF CUSTOMER LIFETIME VALUE WITH DATA MINING TECHNIQUE

Organizations have used Customer Lifetime Value (CLV) as an appropriate pattern to classify their customers. Data mining techniques have enabled organizations to analyze their customers’ behaviors more quantitatively. This research has been carried out to cluster customers based on factors of CLV model including length, recency, frequency, and monetary (LRFM) through data mining. Based on LRFM,...

Mining Spatial Data via Clustering

Contributions from researchers in Knowledge Discovery are producing essential tools in order to better understand the typically large amounts of spatial data in Geographical Information Systems. Clustering techniques are proving to be valuable in providing exploratory analysis functionality while supporting approaches for automated pattern discovery in spatially referenced data and for the iden...

Generalized density based clustering for spatial data mining

On Model Based Clustering in a Spatial Data Mining Context

In this paper we present the finite mixture models approach to clustering of high dimensional data. The mixture resolving approach to cluster analysis has been addressed in a number of different ways; the underlying assumption is that the patterns to be clustered are drawn from one of several distributions, and the goal is to identify the parameters of each and (perhaps) their number. Finite mi...

Spatial Data Mining: Association and Clustering

The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of conventional data...