Accelerated Sequential Data Clustering

IF 1.8 4区 计算机科学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Classification Pub Date : 2024-05-09 DOI:10.1007/s00357-024-09472-4
Reza Mortazavi, Elham Enayati, Abdolali Basiri
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Abstract

Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data, resulting in the problem of clustering sequential data. For instance, analyzing the moving pattern of an object and detecting community structure in a complex network are related to sequential data clustering. The constraint of the continuous region prevents previous clustering algorithms from being directly applied to the problem. A dynamic programming algorithm was proposed to address the issue, which returns the optimal sequential data clustering. However, it is not scalable and hence the practicality is limited. This paper revisits the solution and enhances it by introducing a greedy stopping condition. This condition halts the algorithm’s search process when it is likely that the optimal solution has been found. Experimental results on multiple datasets show that the algorithm is much faster than its original solution while the optimality gap is negligible.

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加速序列数据聚类
数据聚类是数据挖掘领域的一项重要任务。在许多实际应用中,聚类算法必须考虑数据的顺序,从而产生了顺序数据聚类问题。例如,分析物体的移动模式和检测复杂网络中的群落结构都与顺序数据聚类有关。由于连续区域的限制,以往的聚类算法无法直接应用于该问题。为了解决这个问题,有人提出了一种动态编程算法,它能返回最优的顺序数据聚类。但是,该算法不具备可扩展性,因此实用性有限。本文重新审视了这一解决方案,并通过引入贪婪停止条件对其进行了改进。当可能已经找到最优解时,该条件会停止算法的搜索过程。在多个数据集上的实验结果表明,该算法比其原始解决方案要快得多,而优化差距却可以忽略不计。
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来源期刊
Journal of Classification
Journal of Classification 数学-数学跨学科应用
CiteScore
3.60
自引率
5.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: To publish original and valuable papers in the field of classification, numerical taxonomy, multidimensional scaling and other ordination techniques, clustering, tree structures and other network models (with somewhat less emphasis on principal components analysis, factor analysis, and discriminant analysis), as well as associated models and algorithms for fitting them. Articles will support advances in methodology while demonstrating compelling substantive applications. Comprehensive review articles are also acceptable. Contributions will represent disciplines such as statistics, psychology, biology, information retrieval, anthropology, archeology, astronomy, business, chemistry, computer science, economics, engineering, geography, geology, linguistics, marketing, mathematics, medicine, political science, psychiatry, sociology, and soil science.
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