data mining techniques. Whether you refer to betting a few days in advance of the sporting event or live betting, both … options have been taken into consideration. By using a clustering algorithm for analyzing both the database containing events …

This paper review some state-of-the art non-parallel and parallel approaches of data clustering and present the … usefulness of parallel genetic algorithms (PGAs) for clustering. In solving the clustering problem many traditional methods stuck … general it is unknown to the user. Therefore, clustering becomes a trial-and-error work and is also very expensive in terms of …

This paper presents an assessment of the renewable energy potential in Romania using a clustering based data mining … renewable energy potential for electricity generation was mapped into representative zones using K-Means clustering algorithm …

incorporate data dependency in the clustering process. In this research we focus on clustering categorical sequences. The method … proposed here combines model-based and heuristic clustering. In the first step, the categorical sequences are transformed by an …, in the second step, using hierarchical clustering on the matrix of distances, the sequences can be clustered. This paper …

sensor networks are different from those in traditional data mining systems. This paper proposes a novel clustering algorithm …Clustering is applied in wireless sensor networks for increasing energy efficiency. Clustering methods in wireless … clustering in wireless sensor networks and several evaluation metrics are given. MSTME performs better than already known …