| || || Sharma, Anurag.|
| || || Clustering for data mining : a hybrid particle swarm optimization - self organizing map approach|
Institution: University of the South Pacific.
Call No.: pac In Process
Copyright:Under 10% of this thesis may be copied without the authors written permission
Abstract: Self-organizing map (SOM) is a well known data reduction technique used in data mining. It can reveal structure in data sets through data visualization that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOMs, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this thesis, the use of an adaptive heuristic particle swarm optimization (PSO) algorithm is proposed for finding cluster boundaries directly from the code vectors obtained from SOMs. The application of the proposed method to several standard data sets together with real world data set of unlabeled call data for a mobile phone operator demonstrates its feasibility. PSO algorithm utilizes a so-called U-matrix of SOMs to determine cluster boundaries; the results of this novel automatic method compare very favorably to boundary detection through traditional algorithms k-means and hierarchical based approach.