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We define <jats:italic>class discriminative association rules<\/jats:italic> (CDARs) as the <jats:italic>class association rules<\/jats:italic> (CARs) in one data stream that have higher support compared with the same rules in the rest of the data streams. Compared to associative classification mining in a single data stream, there are additional challenges in the discriminative associative classification mining in multiple data streams, as the Apriori property of the subset is not applicable. The proposed single-pass H-DAC algorithm is designed based on distinguishing features of the rules to improve classification accuracy and efficiency. Continuously arriving transactions are inserted at fast speed and large volume, and CDARs are discovered in the tilted-time window model. The data structures are dynamically adjusted in offline time intervals to reflect each rule supported in different periods. Empirical analysis shows the effectiveness of the proposed method in the large fast speed data streams. Good efficiency is achieved for batch processing of small and large datasets, plus 0\u20132% improvements in classification accuracy using the tilted-time window model (i.e., almost with zero overhead). These improvements are seen only for the first 32 incoming batches in the scale of our experiments and we expect better results as the data streams grow.<\/jats:p>","DOI":"10.1007\/s00500-022-07517-7","type":"journal-article","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T13:05:12Z","timestamp":1666443912000},"page":"953-971","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["H-DAC: discriminative associative classification in data streams"],"prefix":"10.1007","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8847-212X","authenticated-orcid":false,"given":"Majid","family":"Seyfi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"issue":"03","key":"7517_CR1","doi-asserted-by":"publisher","first-page":"1450027","DOI":"10.1142\/S0219649214500270","volume":"13","author":"N Abdelhamid","year":"2014","unstructured":"Abdelhamid N, Thabtah F (2014) Associative classification approaches: review and comparison. 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(2) This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. (3) The first author (i.e., Majid Seyfi) has no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. (4) The following author has affiliations with organizations with a direct or indirect financial interest in the subject matter discussed in the manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Hereby, the authors consciously assure that for this manuscript the following is fulfilled: (1) This material is the authors' original work, which has not been previously published elsewhere. (2) The paper is not currently being considered for publication elsewhere. (3) The paper reflects the authors' research and analysis truthfully and completely. (4) The paper properly credits the meaningful contributions of co-authors and co-researchers. (5) The results are appropriately placed in the context of prior and existing research. (6) All sources used are properly disclosed (correct citation). Copying of text must be indicated as such by using quotation marks and giving proper reference. (7) All authors have been personally and actively involved in substantial work leading to the paper and will take public responsibility for its content.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"There is no informed consent applied to this research work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}