论文阅读 [TPAMI-2022] Incremental Density-Based Clustering on Multicore Processors

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搜索论文: Incremental Density-Based Clustering on Multicore Processors

搜索论文: http://www.studyai.com/search/whole-site/?q=Incremental+Density-Based+Clustering+on+Multicore+Processors

关键字(Keywords)

Clustering algorithms; Multicore processing; Databases; Instruction sets; Electronic mail; Time factors; Clustering methods; Density-based clustering; anytime clustering; incremental clustering; active clustering; multicore CPUs

机器学习

聚类

摘要(Abstract)

The density-based clustering algorithm is a fundamental data clustering technique with many real-world applications.

基于密度的聚类算法是一种具有许多实际应用的基本数据聚类技术。.

However, when the database is frequently changed, how to effectively update clustering results rather than reclustering from scratch remains a challenging task.

然而，当数据库频繁更改时，如何有效地更新聚类结果，而不是从头开始重新聚类仍然是一项具有挑战性的任务。.

In this work, we introduce IncAnyDBC, a unique parallel incremental data clustering approach to deal with this problem.

在这项工作中，我们介绍了一种独特的并行增量数据聚类方法IncainDBC来处理这个问题。.

First, IncAnyDBC can process changes in bulks rather than batches like state-of-the-art methods for reducing update overheads.

首先，IncandyDBC可以批量处理更改，而不是像最先进的方法那样批量处理更改，以减少更新开销。.

Second, it keeps an underlying cluster structure called the object node graph during the clustering process and uses it as a basis for incrementally updating clusters wrt.

其次，它在聚类过程中保留了一个称为对象节点图的底层聚类结构，并将其用作增量更新聚类wrt的基础。.

inserted or deleted objects in the database by propagating changes around affected nodes only.

通过仅在受影响的节点周围传播更改，在数据库中插入或删除对象。.

In additional, IncAnyDBC actively and iteratively examines the graph and chooses only a small set of most meaningful objects to produce exact clustering results of DBSCAN or to approximate results under arbitrary time constraints.

此外，IncaydBC会主动、迭代地检查图形，并仅选择一小部分最有意义的对象，以生成DBSCAN的精确聚类结果，或在任意时间约束下近似结果。.

This makes it more efficient than other existing methods.

这使得它比其他现有方法更有效。.

Third, by processing objects in blocks, IncAnyDBC can be efficiently parallelized on multicore CPUs, thus creating a work-efficient method.

第三，通过在块中处理对象，可以在多核CPU上高效地并行IncandyDBC，从而创建一种工作效率高的方法。.

It runs much faster than existing techniques using one thread while still scaling well with multiple threads.

它的运行速度比使用一个线程的现有技术快得多，同时仍然可以很好地扩展多个线程。.

Experiments are conducted on various large real datasets for demonstrating the performance of IncAnyDBC…

在各种大型真实数据集上进行了实验，以演示IncaydBC的性能。。.

作者(Authors)

[‘Son T. Mai’, ‘Jon Jacobsen’, ‘Sihem Amer-Yahia’, ‘Ivor Spence’, ‘Nhat-Phuong Tran’, ‘Ira Assent’, ‘Quoc Viet Nguyen’]