期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 33, 期 4, 页码 1674-1691出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2945573
关键词
Itemsets; Data mining; Databases; Data structures; Task analysis; Benchmark testing; Machine intelligence; Efficient frequent itemsets extraction; efficient data structure; graph utility; maximal frequent itemsets
类别
资金
- GIK Institute graduate programresearch fund under PSS scheme
This research introduces a graph-based approach to represent transactional databases, storing all information relevant to mining FIs in one pass, along with an algorithm for extracting FIs from this structure. Experimental results demonstrate that the proposed approach outperforms other methods in terms of time efficiency.
Frequent itemsets mining is an active research problem in the domain of data mining and knowledge discovery. With the advances in database technology and an exponential increase in data to be stored, there is a need for efficient approaches that can quickly extract useful information from such large datasets. Frequent Itemsets (FIs) mining is a data mining task to find itemsets in a transactional database which occur together above a certain frequency. Finding these FIs usually requires multiple passes over the databases; therefore, making efficient algorithms crucial for mining FIs. This work presents a graph-based approach for representing a complete transactional database. The proposed graph-based representation enables the storing of all relevant information (for extracting FIs) of the database in one pass. Later, an algorithm that extracts the FIs from the graph-based structure is presented. Experimental results are reported comparing the proposed approach with 17 related FIs mining methods using six benchmark datasets. Results show that the proposed approach performs better than others in terms of time.
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