2,172 research outputs found
Parallel Hierarchical Affinity Propagation with MapReduce
The accelerated evolution and explosion of the Internet and social media is
generating voluminous quantities of data (on zettabyte scales). Paramount
amongst the desires to manipulate and extract actionable intelligence from vast
big data volumes is the need for scalable, performance-conscious analytics
algorithms. To directly address this need, we propose a novel MapReduce
implementation of the exemplar-based clustering algorithm known as Affinity
Propagation. Our parallelization strategy extends to the multilevel
Hierarchical Affinity Propagation algorithm and enables tiered aggregation of
unstructured data with minimal free parameters, in principle requiring only a
similarity measure between data points. We detail the linear run-time
complexity of our approach, overcoming the limiting quadratic complexity of the
original algorithm. Experimental validation of our clustering methodology on a
variety of synthetic and real data sets (e.g. images and point data)
demonstrates our competitiveness against other state-of-the-art MapReduce
clustering techniques
Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially.
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging for large datasets. In this paper, we focus on the popular k-center variant which, given a set S of points from some metric space and a parameter k0, the algorithms yield solutions whose approximation ratios are a mere additive term \u3f5 away from those achievable by the best known polynomial-time sequential algorithms, a result that substantially improves upon the state of the art. Our algorithms are rather simple and adapt to the intrinsic complexity of the dataset, captured by the doubling dimension D of the metric space. Specifically, our analysis shows that the algorithms become very space-efficient for the important case of small (constant) D. These theoretical results are complemented with a set of experiments on real-world and synthetic datasets of up to over a billion points, which show that our algorithms yield better quality solutions over the state of the art while featuring excellent scalability, and that they also lend themselves to sequential implementations much faster than existing ones
Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce
The kernel -means is an effective method for data clustering which extends
the commonly-used -means algorithm to work on a similarity matrix over
complex data structures. The kernel -means algorithm is however
computationally very complex as it requires the complete data matrix to be
calculated and stored. Further, the kernelized nature of the kernel -means
algorithm hinders the parallelization of its computations on modern
infrastructures for distributed computing. In this paper, we are defining a
family of kernel-based low-dimensional embeddings that allows for scaling
kernel -means on MapReduce via an efficient and unified parallelization
strategy. Afterwards, we propose two methods for low-dimensional embedding that
adhere to our definition of the embedding family. Exploiting the proposed
parallelization strategy, we present two scalable MapReduce algorithms for
kernel -means. We demonstrate the effectiveness and efficiency of the
proposed algorithms through an empirical evaluation on benchmark data sets.Comment: Appears in Proceedings of the SIAM International Conference on Data
Mining (SDM), 201
Efficient classification using parallel and scalable compressed model and Its application on intrusion detection
In order to achieve high efficiency of classification in intrusion detection,
a compressed model is proposed in this paper which combines horizontal
compression with vertical compression. OneR is utilized as horizontal
com-pression for attribute reduction, and affinity propagation is employed as
vertical compression to select small representative exemplars from large
training data. As to be able to computationally compress the larger volume of
training data with scalability, MapReduce based parallelization approach is
then implemented and evaluated for each step of the model compression process
abovementioned, on which common but efficient classification methods can be
directly used. Experimental application study on two publicly available
datasets of intrusion detection, KDD99 and CMDC2012, demonstrates that the
classification using the compressed model proposed can effectively speed up the
detection procedure at up to 184 times, most importantly at the cost of a
minimal accuracy difference with less than 1% on average
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