{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comparing-dataset-characteristics-that-favor","title":"Comparing Dataset Characteristics that Favor the Apriori, Eclat or FP-Growth Frequent Itemset Mining Algorithms","arxiv_id":"1701.09042","date":"2017-01-30","proceeding":null,"authors":["Jeff Heaton"],"abstract":"Frequent itemset mining is a popular data mining technique. Apriori, Eclat,\nand FP-Growth are among the most common algorithms for frequent itemset mining.\nConsiderable research has been performed to compare the relative performance\nbetween these three algorithms, by evaluating the scalability of each algorithm\nas the dataset size increases. While scalability as data size increases is\nimportant, previous papers have not examined the performance impact of\nsimilarly sized datasets that contain different itemset characteristics. This\npaper explores the effects that two dataset characteristics can have on the\nperformance of these three frequent itemset algorithms. To perform this\nempirical analysis, a dataset generator is created to measure the effects of\nfrequent item density and the maximum transaction size on performance. The\ngenerated datasets contain the same number of rows. This provides some insight\ninto dataset characteristics that are conducive to each algorithm. The results\nof this paper's research demonstrate Eclat and FP-Growth both handle increases\nin maximum transaction size and frequent itemset density considerably better\nthan the Apriori algorithm.\n  This paper explores the effects that two dataset characteristics can have on\nthe performance of these three frequent itemset algorithms. To perform this\nempirical analysis, a dataset generator is created to measure the effects of\nfrequent item density and the maximum transaction size on performance. The\ngenerated datasets contain the same number of rows. This provides some insight\ninto dataset characteristics that are conducive to each algorithm. The results\nof this paper's research demonstrate Eclat and FP-Growth both handle increases\nin maximum transaction size and frequent itemset density considerably better\nthan the Apriori algorithm.","url_abs":"http://arxiv.org/abs/1701.09042v1","url_pdf":"http://arxiv.org/pdf/1701.09042v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"comparing-dataset-characteristics-that-favor","repo_url":"https://github.com/jeffheaton/papers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"comparing-dataset-characteristics-that-favor","repo_url":"https://github.com/alextanhongpin/affinity-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}