Machine learning-based computational intelligence methods are widely used to analyze large-scale data sets in this age of big data. Extracting useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex problem to solve. With high levels of data availability (i.e., Big Data), automatic classification of them has become an important and complex task. Hence, we explore the power of applying MapReduce-based distributed AdaBoosting of extreme learning machine (ELM) to build a predictive bag of classification models. Accordingly, (1) data set ensembles are created; (2) ELM algorithm is used to build weak learners (classifier functions); and (3) builds a strong learner from a set of weak learners. We applied this training model to the benchmark knowledge discovery and data mining data sets.
MapReduce based learning algorithms from distributed data chunks has been studied by many researchers. Many different MapReduce based learning solutions over arbitrary partitioned data have been proposed recently. Some popular MapReduce based solutions to train machine learning algorithms in the literature include the following. proposed a learning tree model which is based on series of distributed computations, and implements each one using the MapReduce model of distributed computation @cite_50 . develops some algorithms using MapReduce to perform parallel data joins on large scale data sets @cite_7 . use batch updating based hierarchical clustering to reduce computational time and data communication @cite_3 . Their approach uses co-occurence based feature selection to remove noisy features and decrease the dimension of the feature vectors. proposed parallel density based clustering algorithm (DBSCAN). They developed a partitioning strategy for large scale non-indexed data with a 4-stages MapReduce paradigm @cite_48 . proposed parallel k-means clustering based on MapReduce @cite_9 . Their approaches focus on implementing k-means with the read-only convergence heuristic in the MapReduce pattern.
@cite_7 @cite_48 @cite_9 @cite_3 @cite_50
2154879298 2061051181 2116762767 2109380209 2125816831
In data mining applications and spatial and multimedia databases, a useful tool is the kNN join, which is to produce the k nearest neighbors (NN), from a dataset S, of every point in a dataset R. Since it involves both the join and the NN search, performing kNN joins efficiently is a challenging task. Meanwhile, applications continue to witness a quick (exponential in some cases) increase in the amount of data to be processed. A popular model nowadays for large-scale data processing is the shared-nothing cluster on a number of commodity machines using MapReduce [6]. Hence, how to execute kNN joins efficiently on large data that are stored in a MapReduce cluster is an intriguing problem that meets many practical needs. This work proposes novel (exact and approximate) algorithms in MapReduce to perform efficient parallel kNN joins on large data. We demonstrate our ideas using Hadoop. Extensive experiments in large real and synthetic datasets, with tens or hundreds of millions of records in both R and S and up to 30 dimensions, have demonstrated the efficiency, effectiveness, and scalability of our methods. Data clustering is an important data mining technology that plays a crucial role in numerous scientific applications. However, it is challenging due to the size of datasets has been growing rapidly to extra-large scale in the real world. Meanwhile, MapReduce is a desirable parallel programming platform that is widely applied in kinds of data process fields. In this paper, we propose an efficient parallel density-based clustering algorithm and implement it by a 4-stages MapReduce paradigm. Furthermore, we adopt a quick partitioning strategy for large scale non-indexed data. We study the metric of merge among bordering partitions and make optimizations on it. At last, we evaluate our work on real large scale datasets using Hadoop platform. Results reveal that the speedup and scale up of our work are very efficient. Data clustering has been received considerable attention in many applications, such as data mining, document retrieval, image segmentation and pattern classification. The enlarging volumes of information emerging by the progress of technology, makes clustering of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel clustering algorithms. In this paper, we propose a parallel k -means clustering algorithm based on MapReduce, which is a simple yet powerful parallel programming technique. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware. Large datasets become common in applications like Internet services, genomic sequence analysis and astronomical telescope. The demanding requirements of memory and computation power force data mining algorithms to be parallelized in order to efficiently deal with the large datasets. This paper introduces our experience of grouping internet users by mining a huge volume of web access log of up to 100 gigabytes. The application is realized using hierarchical clustering algorithms with Map-Reduce, a parallel processing framework over clusters. However, the immediate implementation of the algorithms suffers from efficiency problem for both inadequate memory and higher execution time. This paper present an efficient hierarchical clustering method of mining large datasets with Map-Reduce. The method includes two optimization techniques: “Batch Updating” to reduce the computational time and communication costs among cluster nodes, and “Co-occurrence based feature selection” to decrease the dimension of feature vectors and eliminate noise features. The empirical study shows the first technique can significantly reduce the IO and distributed communication overhead, reducing the total execution time to nearly 1 15. Experimentally, the second technique efficiently simplifies the features while obtains improved accuracy of hierarchical clustering. Classification and regression tree learning on massive datasets is a common data mining task at Google, yet many state of the art tree learning algorithms require training data to reside in memory on a single machine. While more scalable implementations of tree learning have been proposed, they typically require specialized parallel computing architectures. In contrast, the majority of Google's computing infrastructure is based on commodity hardware. In this paper, we describe PLANET: a scalable distributed framework for learning tree models over large datasets. PLANET defines tree learning as a series of distributed computations, and implements each one using the MapReduce model of distributed computation. We show how this framework supports scalable construction of classification and regression trees, as well as ensembles of such models. We discuss the benefits and challenges of using a MapReduce compute cluster for tree learning, and demonstrate the scalability of this approach by applying it to a real world learning task from the domain of computational advertising.