Trends in algorithm design have shown that hybrid algorithms, which combine or merge multiple algorithms, can create synergies to overcome the inherent limitations of the underlying individual algorithms. There are two broad types of hybridization: collaborative - individual algorithms tackle an instance of the problem sequentially or in parallel and exchange information accordingly while solving the problem; integrative - individual algorithms are dedicated to tackling different aspect(s) of the problem-solving process.

In this thesis, we propose a schema for an enhanced form of integrative hybridization that blends granular algorithmic components from multiple algorithms to derive a new singular clustering algorithm. As a case study for the proposed hybridization technique, we examine ant clustering algorithm (a swarm intelligence algorithm that is based on the natural phenomenon of brood sorting in some species of ants); highlight the strengths and weaknesses of the algorithm; and present a blend of algorithmic components from tabu search into the algorithm to improve its solution quality.

Empirical results from applying the blended algorithm to clustering benchmark datasets show improved clustering validation measures for the proposed blended hybrid algorithm compared to other forms of hybridization of the same underlying individual algorithms. Besides, the quality of clusters uncovered by this hybrid algorithm competes favorably with those uncovered using popular clustering algorithms such as DBSCAN and mean shift.

Finally, we show the feasibility and viability of the blended algorithm when used in a novel application of clustering to the estimation of the cost of claims for group insurance benefits.