With the dawn of the Big Data era, data sets are growing rapidly. Data is streaming from everywhere - from cameras, mobile phones, cars, and other electronic devices. Clustering streaming data is a very challenging problem. Unlike the traditional clustering algorithms where the dataset can be stored and scanned multiple times, clustering streaming data has to satisfy constraints such as limit memory size, real-time response, unknown data statistics and an unknown number of clusters. In this paper, we present a novel online clustering algorithm which can be used to cluster streaming data without knowing the number of clusters a priori. Results on both synthetic and real datasets show that the proposed algorithm produces partitions which are close to what you could get if you clustered the whole data at one time.
A density-based online clustering method is the DenStream @cite_8 . The DenStream algorithm extends the notion of core points of the DBSCAN algorithm to a new concept of micro-clusters. It also has two phases. In the online phase, the algorithm maintains micro clusters structures which approximately capture the density of the data stream. In order to create the final data partition, in the offline phase, a variant of the DBSCAN algorithm is applied to those clusters. The DenStream algorithm should be provided with a cluster radius threshold and data fading rate. Providing the cluster radius is a main drawback of the algorithm. As stated, in streaming data, the data statistics may vary along the time and therefore a single cluster radius may not fit all the data.
@cite_8
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Clustering is one of the basic data mining tasks that can be used to extract hidden information from data in the absence of target classes. One of the most well-known density based clustering algorithms for processing spatial data is Density-Based Spatial Clustering of Application with Noise (DBSCAN) that uses learning parameters epsiv and minPts to define the density that will be sought in the data set while forming the clusters. The major drawbacks of the DBSCAN algorithm are its sensitivity to user input required to execute the algorithm, inability to recognize clusters with different densities, and computational complexity. In this study, we propose a three-level clustering method to address the second issue. The first level selects appropriate grids so that the density is homogeneous in each grid. The second stage merges cells with similar densities and identifies the most suitable values of epsiv and minPts in each grid that remain after merging. The third step of the proposed method executes the DBSCAN method with these identified parameters in the dataset. The proposed method is tested in three artificial benchmark data sets to demonstrate that the clusters are correctly identified. Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most well-known density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points. This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial-temporal data warehouse system designed for storing and clustering a wide range of spatial-temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results. The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘shared-nothing’ architecture with multiple computers interconnected through a network. A fundamental component of a shared-nothing system is its distributed data structure. We introduce the dRa-tree, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer. We implemented our method using a number of workstations connected via Ethernet (10 Mbit). A performance evaluation shows that PDBSCAN offers nearly linear speedup and has excellent scaleup and sizeup behavior.