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Dealing with distributed data is one of the challenges for clustering, as most clustering techniques require the data to be centralized. One of them, k -means, has been elected as one of the most influential data mining algorithms for being simple, scalable, and easily modifiable to a variety of contexts and application domains. However, exact distributed versions of k -means are still sensitive to the selection of the initial cluster prototypes and require the number of clusters to be specified in advance. Additionally, preserving data privacy among repositories may be a complicating factor. In order to overcome k -means limitations, two different approaches were adopted in this paper: the first obtains a final model identical to the centralized version of the clustering algorithm and the second generates and selects clusters for each distributed data subset and combines them afterwards. It is also described how to apply the algorithms compared while preserving data privacy. The algorithms are compared experimentally from two perspectives: the theoretical one, through asymptotic complexity analyses, and the experimental one, through a comparative evaluation of results obtained from a collection of experiments and statistical tests. The results obtained indicate which algorithm is more suitable for each application scenario. [ABSTRACT FROM AUTHOR]

Fuzzy models within the framework of orthonormal basis functions (OBF fuzzy models) have been introduced in previous works and shown to be a very promising approach to the areas of nonlinear system identification and control, since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. As fuzzy models, however, they exhibit the dimensionality problem which is the main drawback to the application of neural networks and fuzzy systems to the modeling and control of large-scale systems. This problem has successfully been dealt with in the literature by means of hierarchical structures composed of submodels connected in cascade. In the present paper a hierarchical fuzzy model within the OBF framework is presented. A data-driven hybrid identification method based on genetic and gradient-based algorithms is described in details. A model-based predictive control scheme is also presented and applied to control of a complex industrial process for ethyl alcohol (ethanol) production. [Copyright &y& Elsevier]

The recent growing size of datasets requires scalability of data mining algorithms, such as clustering algorithms. The MapReduce programing model provides the scalability needed, alongside with portability as well as automatic data safety and management. k -means is one of the most popular algorithms in data mining and can be easily adapted to the MapReduce model. Nevertheless, k -means has drawbacks, such as the need to provide the number of clusters ( k ) in advance and the sensitivity of the algorithm to the initial cluster prototypes. This paper presents two evolutionary scalable metaheuristics in MapReduce that automatically seek the solution with the optimal number of clusters and best clustering structure for scalable datasets. The first consists in an algorithm able to iteratively enhance k -means clusterings through evolutionary operators designed to handle distributed data. The second consists in applying evolutionary k -means to cluster each distributed portion of a dataset in an independent way, combining the obtained results into an ensemble afterwards. The proposed techniques are compared asymptotically and experimentally with other state-of-the-art clustering algorithms also developed in MapReduce. The results are analyzed by statistical tests and show that the first proposed metaheuristic yielded results with the best quality, while the second achieved the best computing times. [ABSTRACT FROM AUTHOR]