Implementing Parallel processing of DBSCAN with Map reduce

Implementing Parallel processing of DBSCAN with Map reduce

Implementing Parallel processing of DBSCAN with Map reduce

Hartman, Bobbie, Contributing Writer has reference to this Academic Journal, PHwiki organized this Journal Garrett Poppe, Liv Nguekap, Adrian MirabelCSUDH, Computer Science DepartmentImplementing Parallel processing of DBSCAN with Map reduceIntroduction to the topicHistory in addition to related workProblem definitionExisting approaches to solving the problemDescription of proposed algorithmProblems in addition to solutionsThe trend of the fieldConclusionOverviewDensity-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed 1996.[1] “It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away).” “DBSCAN is one of the most common clustering algorithms in addition to also most cited in scientific literature.”[2]“In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory in addition to practice) at the leading data mining conference, KDD.”[3]Introduction

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IntroductionMotivationCensus survey dataMotivationFace recognition(FaceVACS-DBScan)

MotivationMining Biomedical Images with Density-based ClusteringMotivationSatellite image recognitionProblemO(nlog(n)) Best caseO(n²) Worst caseCurrent algorithms are done as a single taskAlgorithm starts with first point in addition to continues comparing to last pointRequires user to input minPts in addition to EpsParallelization of DBSCAN is challenging as it exhibits an inherent sequential data access order.

Approaches to solve problemPDSDBSCAN using graph algorithmic concepts in addition to using a tree-based bottom-up approach to construct the clusters, yields a better balanced workload distribution. Implementation of the algorithm both as long as shared in addition to as long as distributed memory.CURE utilizes multiple representative points as long as each cluster that are generated by selecting well scattered points from the cluster in addition to then shrinking them toward the center of the cluster by a specified fraction. This enables CURE to adjust well to the geometry of clusters having non-spherical shapes in addition to wide variances in size. CurrentProposed Algorithm (Data Set)

Proposed Algorithm (Data Set)Proposed Algorithm (Map Tasks)Proposed Algorithm (Map Tasks)

Proposed Algorithm (Map Tasks)Proposed Algorithm (Map Function)Proposed Algorithm (Map Function Results)

Proposed Algorithm (Reduce Function)If MIN-pts = 2Start at first cluster table.Visit each cluster within table.Add all points from visited table to first cluster table.When all points are visited go to next unvisited cluster table.Repeat step 1 until all tables are visited.Omit any noise tables (a cluster table with less than 2 points).Proposed Algorithm (Reduce Function)Proposed Algorithm (Final Clusters)

Proposed Algorithm (Final Clusters)Proposed Algorithm (MIN-pts)Clusters that do not contain the minimum number of points within the EPS-min, will be dropped during the reduce phase.If MIN-pts = 4Check ptsCntr as long as each cluster table visited in addition to add only ptsCtr if it is > 4Proposed Algorithm (MIN-pts)

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Proposed Algorithm (MIN-pts)Anticipated problems in addition to solutionsDataset is too large as long as memory of a single node.Split dataset into portions where the origin point is compared with each split during the map phase.Combine all clusters created from split dataset during the reduce phase.Trends in addition to future researchBig Data requires parallel processingData collected is outgrowing processing powerMachine learning in addition to AI can fill the need as long as analysis of large amounts of data

References[1] Ester, Martin; Kriegel, Hans-Peter; S in addition to er, Jörg; Xu, Xiaowei (1996). Simoudis, Evangelos; Han, Jiawei; Fayyad, Usama M., eds. A density-based algorithm as long as discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery in addition to Data Mining (KDD-96). AAAI Press. pp. 226–231. ISBN 1-57735-004-9. CiteSeerX:[2] Most cited data mining articles according to Microsoft academic search; DBSCAN is on rank 24, when accessed on: 4/18/2010[3] “2014 SIGKDD Test of Time Award”. ACM SIGKDD. 2014-08-18. Retrieved 2014-08-22.

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