Kernel machines such as Support Vector Machines (SVMs) have been widely used in various data mining applications with good generalization properties. Performance of SVMs for solving nonlinear problems is highly affected by kernel functions. The complexity of SVMs training is mainly related to the size of a training dataset. How to design a powerful kernel, how to speed up SVMs training and how to train SVMs with millions of examples are still challenging problems in the SVMs research. For these important problems, powerful and flexible kernel trees called Evolutionary Granular Kernel Trees (EGKTs) are designed to incorporate prior domain knowledge. Granular Kernel Tree Structure Evolving System (GKTSES) is developed to evolve the structures of Granular Kernel Trees (GKTs) without prior knowledge. A voting scheme is also proposed to reduce the prediction deviation of GKTSES. To speed up EGKTs optimization, a master-slave parallel model is implemented. To help SVMs challenge large-scale data mining, a Minimum Enclosing Ball (MEB) based data reduction method is presented, and a new MEB-SVM algorithm is designed. All these kernel methods are designed based on Granular Computing (GrC). In general, Evolutionary Granular Kernel Machines (EGKMs) are investigated to optimize kernels effectively, speed up training greatly and mine huge amounts of data efficiently.

The unprecedented and exponential growth of data along with the advent of multi-core processors has triggered a massive paradigm shift from traditional single threaded programming to parallel programming. A number of parallel programming paradigms have thus been proposed and have become pervasive and inseparable from any large production environment. Also with the massive amounts of data available and with the ever increasing business need to process and analyze this data quickly at the minimum cost, there is much more demand for implementing fast data mining algorithms on cheap hardware. This thesis explores a parallel programming model called dataflow, the essence of which is computation organized by the flow of data through a graph of operators. This paradigm exhibits pipeline, horizontal and vertical parallelism and requires only the data of the active operators in memory at any given time allowing it to scale easily to very large datasets. The thesis describes the dataflow implementation of two data mining applications on huge datasets. We first develop an efficient dataflow implementation of a Collaborative Filtering (CF) algorithm based on weighted co-clustering and test its effectiveness on a large and sparse Netflix data. This implementation of the recommender system was able to rapidly train and predict over 100 million ratings within 17 minutes on a commodity multi-core machine. We then describe a dataflow implementation of a non-parametric density based clustering algorithm called Auto-HDS to automatically detect small and dense clusters on a massive astronomy dataset. This implementation was able to discover dense clusters at varying density thresholds and generate a compact cluster hierarchy on 100k points in less than 1.3 hours. We also show its ability to scale to millions of points as we increase the number of available resources. Our experimental results illustrate the ability of this model to “scale” well to massive datasets and its ability to rapidly discover useful patterns in two different applications. / text