Data Mining - Concepts and Techniques.

Learning And Soft Computing Support Vector Machines Neural Networks And Fuzzy Logic Models

Data uncertainty is an inherent property in various applications due to reasons such as outdated sources or imprecise measurement. When data mining techniques are applied to these data, their uncertainty has to be considered to obtain high quality results. We present UK-means clustering, an algorithm that enhances the K-means algorithm to handle data uncertainty. We apply UK-means to the particular pattern of moving-object uncertainty. Experimental results show that by considering uncertainty, a clustering algorithm can produce more accurate results.

Uncertain data mining : An example in clustering location data

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently, there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services, such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The growing interest in applying unsupervised learning techniques in networking stems from their great success in other fields, such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). In addition, unsupervised learning can unconstrain us from the need for labeled data and manual handcrafted feature engineering, thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting recent advancements in unsupervised learning techniques, and describe their applications in various learning tasks, in the context of networking. We also provide a discussion on future directions and open research issues, while identifying potential pitfalls. While a few survey papers focusing on applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in the literature. Through this timely review, we aim to advance the current state of knowledge, by carefully synthesizing insights from previous survey papers, while providing contemporary coverage of the recent advances and innovations.

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Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects

Computational fluid dynamics (CFD) plays an essential role to analyze fluid flows and heat transfer situations by using numerical methods. Turbomachines involve internal and external fluid flow problems in compressors and turbines. CFD at present is one of the most important tools to design and analyze all types of turbomachinery. The main purpose of this paper is to review the state of the art work carried out in the field of turbomachinery using CFD. Literature review of research work pertaining to CFD analysis in turbines, compressors and centrifugal pumps are described. Various issues of CFD codes used in turbomachinery and its parallelization strategy adopted are highlighted. Furthermore, the prevailing merits and demerits of CFD in turbomachinery are provided. Open areas pertinent to CFD investigation in turbomachinery and CFD code parallelization are also described.

Computational Fluid Dynamics in Turbomachinery: A Review of State of the Art

Computational fluid dynamics (CFD) is one of the most emerging fields of fluid mechanics used to analyze fluid flow situation. This analysis is based on simulations carried out on computing machines. For complex configurations, the grid points are so large that the computational time required to obtain the results are very high. Parallel computing is adopted to reduce the computational time of CFD by utilizing the available resource of computing. Parallel computing tools like OpenMP, MPI, CUDA, combination of these and few others are used to achieve parallelization of CFD software. This article provides a comprehensive state of the art review of important CFD areas and parallelization strategies for the related software. Issues related to the computational time complexities and parallelization of CFD software are highlighted. Benefits and issues of using various parallel computing tools for parallelization of CFD software are briefed. Open areas of CFD where parallelization is not much attempted are identified and parallel computing tools which can be useful for parallelization of CFD software are spotlighted. Few suggestions for future work in parallel computing of CFD software are also provided.

Parallelization Strategies for Computational Fluid Dynamics Software: State of the Art Review

Clustering techniques are widely used in Web Usage Mining to capture similar interests and trends among users accessing a Web site. For this purpose, web access logs generated at a particular web site are preprocessed to discover the user navigational sessions. Clustering techniques are then applied to group the user session data into user session clusters, where intercluster similarities are minimized while the intra cluster similarities are maximized. Since the application of different clustering algorithms generally results in different sets of cluster formation, it is important to evaluate the performance of these methods in terms of accuracy and validity of the clusters, and also the time required to generate them, using appropriate performance measures. This paper describes various validity and accuracy measures including Dunn's Index, Davies Bouldin Index, C Index, Rand Index, Jaccard Index, Silhouette Index, Fowlkes Mallows and Sum of the Squared Error (SSE). We conducted the performance evaluation of the following clustering techniques: k-Means, k-Medoids, Leader, Single Link Agglomerative Hierarchical and DBSCAN. These techniques are implemented and tested against the Web user navigational data. Finally their performance results are presented and compared.

Quantitative Evaluation of Performance and Validity Indices for Clustering the Web Navigational Sessions

https://digitalcommons.aaru.edu.jo/cgi/viewcontent.cgi?article=1059&context=fcij

An analysis of MapReduce efficiency in document clustering using parallel K-means algorithm

https://digitalcommons.aaru.edu.jo/cgi/viewcontent.cgi?article=1062&context=fcij

Zahid Ansari

Papers

Computational Fluid Dynamics in Turbomachinery: A Review of State of the Art

Parallelization Strategies for Computational Fluid Dynamics Software: State of the Art Review

Quantitative Evaluation of Performance and Validity Indices for Clustering the Web Navigational Sessions

An analysis of MapReduce efficiency in document clustering using parallel K-means algorithm

Partition based clustering of large datasets using MapReduce framework: An analysis of recent themes and directions