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Action is any meaningful movement of the human and it is used to convey information or to interact naturally without any mechanical devices. Human action recognition is motivated by some of the applications such as video retrieval, Human robot interaction, to interact with deaf and dumb people etc. In any Action Recognition System, some pre-processing steps(More)
In this paper, we propose an algorithm, DOPTICS, a parallelized version of a popular density based cluster-ordering algorithm OPTICS. Parallelizing OPTICS is challenging because of its strong sequential data access behavior. To achieve high parallelism, a data parallel approach that exploits the underlying indexing structure is proposed. We implement the(More)
Parallelizing algorithms to leverage multiple cores in a processor or multiple nodes in a cluster setup is the only way forward to handle ever-increasing volumes of data. OPTICS is a well-known density based clustering algorithm to identify arbitrary shaped clusters. Since, hierarchical cluster ordering of OPTICS is sensitive to the order in which data is(More)
Clustering is a popular data mining technique which discovers structure in unlabeled data by grouping objects together on the basis of a similarity criterion. Traditional similarity measures lose their meaning as the number of dimensions increases and as a consequence, distance or density based clustering algorithms become less meaningful. Shared Nearest(More)
Handling and processing of larger volume of data requires efficient data mining algorithms. k-means is a very popular clustering algorithm for data mining, but its performance suffers because of initial seeding problem. The computation time of k-means algorithm is directly proportional to the number of data-points, number of dimensions, and number of(More)
Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. To the best of our knowledge, all existing parallel(More)
Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time complexity and inherent data dependencies, it does not scale well for large datasets. In this paper, we parallelize an efficient(More)
A peer-to-peer (P2P) system is composed of peer computers which are interconnected in overlay networks. Here, each peer computer can play both the roles of server and client. The P2P system is fully distributed and there is no centralized coordinator. The power consumption of each peer will vary accordingly. Here, the calculation of CPU utilization time is(More)
DBSCAN is one of the most popular density-based clustering algorithm capable of identifying arbitrary shaped clusters and noise. It is computationally expensive for large data sets. In this paper, we present a grid-based DBSCAN algorithm, GridDBSCAN, which is significantly faster than the state-of-the-art sequential DBSCAN. The efficiency of GridDBSCAN is(More)
Clustering is a popular data mining and machine learning technique which discovers interesting patterns from unlabeled data by grouping similar objects together. Clustering high-dimensional data is a challenging task as points in high dimensional space are nearly equidistant from each other, rendering commonly used similarity measures ineffective. Subspace(More)
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