Please use this identifier to cite or link to this item:
Title: Clustering techniques for web mining
Authors: Sanusi, Emil.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2010
Abstract: The project mainly focuses on the design and implementation of two newly proposed data clustering algorithms, namely Active Fuzzy Constrained Clustering (AFCC) and Semi-Supervised Heuristic Fuzzy Co-clustering with the Ruspini’s condition (SS-HFCR), for semi-supervised data analysis. The Active Fuzzy Constrained Clustering (AFCC) technique tries to take into account simple yet useful constraints provided by the user to “steer” the clustering process to produce more preferred results. Not only AFCC, seeing the possibility of enhancing the newly proposed algorithm namely Heuristic Fuzzy Co-clustering with the Ruspini’s condition (HFCR) with the same fashion as AFCC, an improved Semi-Supervised HFCR (SS-HFCR) is then developed. After being successfully implemented both algorithms in Java, experimental studies have been conducted to both verify the coding and the performance of both algorithms. As to qualify the performance, analyses on the precision, recall and purity measurement were carried out.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
  Restricted Access
997.75 kBAdobe PDFView/Open

Page view(s) 20

checked on Oct 22, 2020

Download(s) 20

checked on Oct 22, 2020

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.