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Mohamed Gresha's Projects

adaptive-competitive-learning-neural-network-hybrid-with-other-algorithms icon adaptive-competitive-learning-neural-network-hybrid-with-other-algorithms

In this section, an Adaptive Competitive Learning Neural Network hybrid with other algorithms has been proposed to cluster big input datasets. The first phase of the proposed algorithm divides big datasets into equal partitions (sub-datasets). In the second phase, the ACLNN algorithm is implemented using a parallel processing technique. As a result of this phase, the number of clusters is determined. The last phase uses this number of clusters for clustering the original big dataset.

adaptive-landmark-based-spectral-clustering-alsc- icon adaptive-landmark-based-spectral-clustering-alsc-

In this section, an algorithm for determining the optimal number of clusters (kopt) is proposed. This number (kopt) is used in the clustering of an input big dataset. This algorithm is referred to as the ALSC algorithm. The proposed algorithm comprises three phases and uses parallel processing to speed up its performance. The main advantages of the proposed algorithm are determining the optimal number of clusters and producing high clustering accuracy with the identification of the number of clusters.

code-kmeans-benchmark icon code-kmeans-benchmark

Code for the manuscript: "An empirical comparison between stochastic and deterministic centroid initialisation for K-Means variations"

parallel-adaptive-competitive-neural-network-paclnn- icon parallel-adaptive-competitive-neural-network-paclnn-

This section is introducing the way of use the parallel processing to de-velop the ACLNN algorithm. Instead of testing a number of clustering struc-tures sequentially to select the best one according to a selection criterion in the ACLNN algorithm, the new proposed PACLNN algorithm does this task in parallel using parallel processing and multi-core processors. Every clus-tering structure is tested in a thread that is run in a worker or a core in a mul-ti-core processor. These threads of execution are run in parallel i.e., concur-rently. Evaluation of the PACLNN algorithm shows that it is faster than the ACLNN algorithm with large or big data sets. Also, it uses the hardware re-sources available in modern multi-core processors better than the ACLNN algorithm.

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