English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

DBSCAN clustering algorithm uses fixed Eps and Minpts. When density distribution is uneven, the effect of clustering is not ideal, and the time complexity of the algorithm is O (n 2 ). To solve the above problems, this paper proposes a parallel grid clustering algorithm and two cluster merging strategies of DBSCAN based on Spark platform, will find the Eps neighborhood to narrow the scope of the eight adjacent cells within the data object, and the parallel execution of the local clustering data with fast global clustering. The experiment shows that the improved DBSCAN parallel algorithm has better acceleration ratio and extensibility. Introduction The rapid development of the Internet and information technology has led to an exponential growth in data volume. How to cluster these massive data quickly and efficiently is a hotspot of data mining in the field of artificial intelligence and machine learning. From the perspective of machine learning, clustering is unsupervised learning, which can automatically discover the similarity between data sources, aggregate data with high similarity into a cluster, and separate dissimilar data as much as possible. However, in the face of large-scale data, traditional single-machine clustering algorithms have not been able to meet the processing needs of massive information in terms of efficiency and computational complexity. In recent years, with the open source of cloud computing platforms such as Hadoop and Spark, the cloud computing parallel programming framework is adopted, and the traditional data mining algorithm is distributed and parallelized to improve the efficiency of big data mining. It has become a hotspot of data mining [1,4]. this paper introduces the Yarn resource manager to improve the traditional DBSCAN algorithm in parallel, so that it can run on the Spark platform effectively, and improve the clustering efficiency and application scope of the algorithm. Spark Parallel Computing Framework Spark is a master-slave architecture platform. The master node is used to control the cluster to ensure the normal operation of the cluster. The slave node acts as a compute node and accepts commands and status reports from the master node. The user submits the application through the client, the master node finds a slave node to start the Driver program, and the driver applies the resource to the resource manager (Cluster Manager) to convert the application into a directed acyclic graph (RDD DAG), and The task information is generated after being converted to the DAG Scheduler, and the Task Scheduler is responsible for submitting the task to the control computing node (Worker), and the Worker starts multiple Executors to execute each task in parallel [3,5]. DBSCAN Algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm proposed by Ester Martin et al. to spatially cluster data object density as a similarity index [2]. DBSCAN refers to the largest set of density-connected points as clusters. Clusters can generate clusters of arbitrary

Research on Parallel Design of DBSCAN Clustering Algorithm in Spatial Data Mining