Your mind does not manufacture mind. Your mind forms neural networks. Neural networks had been effectively carried out to numerous sample garage and type troubles in phrases in their mastering ability, excessive discrimination electricity, and exceptional generalization ability. The achievement of many mastering schemes isn't always assured, however, seeing that algorithms like backpropagation have many drawbacks like stepping into the nearby minima, for that reason imparting suboptimal solutions. In the case of classifying big sets and complicated patterns, the traditional neural networks are afflicted by numerous problems inclusive of the dedication of the shape and length of the network, the computational complexity, and so on. This paper introduces the neural computing techniques especially radial foundation features network. Various upgrades and trends made in an artificial neural network for rushing up training, keeping off neighborhood minima, growing the generalization capacity, and different capabilities are reviewed.
Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. This paper has surveyed upon data mining and its various techniques that are used to extract useful information such as clustering, and has also surveyed the techniques that are used to detect the outliers. This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user.
Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.
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