Reporting and analyzing alternative clustering solutions by employing multi-objective genetic algorithm and conducting experiments on cancer data

Contributors: Reda Alhajj, PhD, Mohamad Elzohbi, Peter Peng

Knowledge-Based Systems 56 (2004) 108-122

Peter Penga, Omer Addama, Mohamad Elzohbia, Sibel T. Özyerb, Ahmad Elhajjc, Shang Gaoa, Yimin Liua, Tansel Özyerd, Mehmet Kayae, Mick Ridleyc, Jon Roknea, Reda Alhajja, f


Clustering is an essential research problem which has received considerable attention in the research community for decades. It is a challenge because there is no unique solution that fits all problems and satisfies all applications. We target to get the most appropriate clustering solution for a given application domain. In other words, clustering algorithms in general need prior specification of the number of clusters, and this is hard even for domain experts to estimate especially in a dynamic environment where the data changes and/or become available incrementally. In this paper, we described and analyze the effectiveness of a robust clustering algorithm which integrates multi-objective genetic algorithm into a framework capable of producing alternative clustering solutions; it is called Multi-objective K-Means Genetic Algorithm (MOKGA). We investigate its application for clustering a variety of datasets, including microarray gene expression data. The reported results are promising. Though we concentrate on gene expression and mostly cancer data, the proposed approach is general enough and works equally to cluster other datasets as demonstrated by the two datasets Iris and Ruspini. After running MOKGA, a pareto-optimal front is obtained, and gives the optimal number of clusters as a solution set. The achieved clustering results are then analyzed and validated under several cluster validity techniques proposed in the literature. As a result, the optimal clusters are ranked for each validity index. We apply majority voting to decide on the most appropriate set of validity indexes applicable to every tested dataset. The proposed clustering approach is tested by conducting experiments using seven well cited benchmark data sets. The obtained results are compared with those reported in the literature to demonstrate the applicability and effectiveness of the proposed approach.

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Dr. Nawaid Usmani and team receive funding for their PRIME study!

The PRIME Study – Prevention and Intervention for MEtabolic syndrome:

Androgen deprivation therapy (ADT), and newer manipulations of androgen receptor signaling have improved outcomes for advanced prostate cancer (PCa) patients.  The toxicities of ADT are many, including an increased risk of developing metabolic syndrome (MS; defined as at least 3 of: hyperglycemia; abdominal obesity; hypertriglyceridemia; reduced HDL cholesterol; and/or hypertension). MS is associated with an increased risk of diabetes, cardiovascular disease mortality, stroke mortality, and all-cause mortality.  The prevalence of MS in men receiving ADT is at least 50% and contributes to decreased quality of life and increased non-cancer-related mortality.  Metformin holds promise as a countermeasure to MS development, and also has been shown to suppress PCa growth in pre-clinical models.

We hypothesize that the addition of metformin to ADT will reduce the rates of MS in men with advanced PCa, diminishing important toxicities of a therapy universally used in advanced disease.

We propose a double-blind, randomized phase III study of metformin or placebo in men with PCa starting intermittent ADT. The primary endpoint is the difference in MS rates at 1 year.  Other aims include evaluation of the influence of metformin on: individual MS components at additional time points; mean serum insulin levels and measures of insulin resistance; weight and quality of life.

A finding that metformin reduces MS incidence and/or has other benefits would change practice, as it would provide a practical and inexpensive strategy to reduce toxicity of an intervention employed in most men with advanced PCa.

- Catalina Vasquez

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