Mining discriminative items in multiple data streams

Lin, Zhenhua; Jiang, Bin; Pei, Jian; Jiang, Daxin

doi:10.1007/s11280-010-0094-0

Cited by 9 publications

(3 citation statements)

References 51 publications

(56 reference statements)

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“…Discriminative itemsets in the tilted-time window model are frequent in one data stream and their frequency in that stream is much higher than that of the rest of the streams, in different periods (Lin et al 2010;Seyfi et al 2017Seyfi et al , 2021a. We discover class discriminative association rules (CDARs), in the tilted-time window model, out of discriminative itemsets based on discriminative value, minimum confidence and minimum support thresholds, in different periods.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to CARs, they exclude the rules which are dominant in more than one data stream. There are fast algorithms proposed for mining discriminative items (Lin et al 2010;Seyfi 2011) discriminative itemsets in static datasets (Seyfi et al 2014(Seyfi et al , 2017(Seyfi et al , 2021a, and in data streams (Seyfi 2018;Seyfi et al 2021a, b).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, they can be effectively used in web page document classification as well as in the personalization of search engines and news delivery services. The frequent patterns are generally frequent in all data streams and may not be distinctive (Lin et al 2010). Analyzing the clickstream data for identifying the web pages that are visited by a specific user (or a group of users) more frequently than other users (or groups), can lead to improved personalized services.…”

Section: Introductionmentioning

confidence: 99%

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H-DAC: discriminative associative classification in data streams

Seyfi

2022

Soft Comput

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In this paper, we propose an efficient and highly accurate method for data stream classification, called discriminative associative classification. We define class discriminative association rules (CDARs) as the class association rules (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 0–2% 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.

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Section: Introductionmentioning

confidence: 99%