hierarchy++:改进了分层聚类算法

IF 0.4 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Data Mining Modelling and Management Pub Date : 2023-01-01 DOI:10.1504/ijdmmm.2023.132975

Wallace Anacleto Pinheiro, Ana Bárbara Sapienza Pinheiro

引用次数: 0

摘要

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Hierarchical++: improving the hierarchical clustering algorithm

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来源期刊

International Journal of Data Mining Modelling and Management COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

1.10

自引率

0.00%

发文量

期刊介绍： Facilitating transformation from data to information to knowledge is paramount for organisations. Companies are flooded with data and conflicting information, but with limited real usable knowledge. However, rarely should a process be looked at from limited angles or in parts. Isolated islands of data mining, modelling and management (DMMM) should be connected. IJDMMM highlightes integration of DMMM, statistics/machine learning/databases, each element of data chain management, types of information, algorithms in software; from data pre-processing to post-processing; between theory and applications. Topics covered include: -Artificial intelligence- Biomedical science- Business analytics/intelligence, process modelling- Computer science, database management systems- Data management, mining, modelling, warehousing- Engineering- Environmental science, environment (ecoinformatics)- Information systems/technology, telecommunications/networking- Management science, operations research, mathematics/statistics- Social sciences- Business/economics, (computational) finance- Healthcare, medicine, pharmaceuticals- (Computational) chemistry, biology (bioinformatics)- Sustainable mobility systems, intelligent transportation systems- National security