8 A Review of Major Application Areas of Differential Evolution <s> The Unsupervised k-Windows Clustering Algorithm <s> The extraction of meaningful information from large collections of data is a fundamental issues in science. To this end, clustering algorithms are typically employed to identify groups (clusters) of similar objects. A critical issue for any clustering algorithm is the determination of the number of clusters present in a dataset. In this contribution we present a clustering algorithm that in addition to partitioning the data into clusters, it approximates the number of clusters during its execution. We further present modifications of this algorithm for different distributed environments, and dynamic databases. Finally, we present a modification of the algorithm that exploits the fractal dimension of the data to partition the dataset. <s> BIB001 </s> 8 A Review of Major Application Areas of Differential Evolution <s> The Unsupervised k-Windows Clustering Algorithm <s> Clustering algorithms typically assume that the available data constitute a random sample from a stationary distribution. As data accumulate over time the underlying process that generates them can change. Thus, the development of algorithms that can extract clustering rules in non-stationary environments is necessary. In this paper, we present an extension of the k-windows algorithm that can track the evolution of cluster models in dynamically changing databases, without a significant computational overhead. Experiments show that the k-windows algorithm can effectively and efficiently identify the changes on the pattern structure. <s> BIB002
The recently proposed k-windows clustering algorithm BIB001 uses a windowing technique to discover the clusters present in an n-dimensional dataset. More specifically, assuming that the dataset lies in n dimensions, the algorithm initializes a number of n-dimensional windows (boxes) over the dataset. Subsequently, it iteratively perturbs these windows using the movement and enlargement procedures, in order to capture within each window patterns that belong to a single cluster. The movement and enlargement procedures are guided by the points that lie within each window. As soon as the movement and enlargement procedures do not significantly increase the number of points within each window they terminate. The final set of windows defines the clustering result of the algorithm. A fundamental issue in cluster analysis, independent of the particular clustering technique applied, is the determination of the number of clusters present in a dataset. The unsupervised k-windows algorithm is capable to determine the number of clusters through a generalization of the original algorithm BIB001 . Finally, it must be noted that no objective function evaluations are necessary during the operation of the k-windows clustering algorithm BIB002 .