Evolutionary clustering based vector quantization and SPIHT coding for image compression

On Combining Nature-Inspired Algorithms for Data Clustering

This chapter proposed different hybrid clustering methods based on combining particle swarm optimization (PSO), gravitational search algorithm (GSA) and free parameters central force optimization (CFO) with each other and with the k-means algorithm. The proposed methods were applied on 5 real datasets from the university of California, Irvine (UCI) machine learning repository. Comparative analysis was done in terms of three measures; the sum of intra cluster distances, the running time and the distances between the clusters centroids. The initial population for the used algorithms were enhanced to minimize the sum of intra cluster distances. Experimental results show that, increasing the number of iterations doesn't have a noticeable impact on the sum of intra cluster distances while it has a negative impact on the running time. K-means combined with GSA (KM-GSA), PSO combined with GSA (PSO-GSA) gave the best performance according to the sum of intra cluster distances while K-means combined with PSO (KM-PSO) and KM-GSA were the best in terms of the running time. Finally, KM-GSA and GSA have the best performance.

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems

Unsupervised data classification (or clustering) analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO) algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Among the data clustering algorithms, the k-means (KM) algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means (KHM) clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization (MPSO) algorithm and KHM clustering. In the proposed algorithm, MPSO is equipped with the cuckoo search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence, and escape from local optima. MPSO updates the positions of particles based on a combination of global worst, global best with personal worst, and personal best to dynamically be used in each iteration of the MPSO. The experimental result on eight real-world data sets and two artificial data sets confirms that this modified version is superior to KHM and the regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy, and correctness while markedly improving the processing time.