A novel multi-core algorithm for frequent itemsets mining in data streams

Bustio-Martínez, Lázaro; Muñoz-Briseño, Alfredo; Cumplido, René; Hernández-León, Raudel; Feregrino-Uribe, Claudia

doi:10.1016/j.patrec.2019.05.003

Cited by 7 publications

(7 citation statements)

References 16 publications

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“…English Education e generally accepted concept is a way for people to achieve their learning effect through the Internet [14]. Some scholars believe that E-learning behavior is also a way of online autonomous learning behavior, distance learning behavior, or online learning.…”

Section: Design Of Student Behavior Model For Onlinementioning

confidence: 99%

Analysis of Students’ Behavior in English Online Education Based on Data Mining

Wang

2021

Mobile Information Systems

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With the formation of global economic integration for better exchange and cooperation with nations around the world, mastering English is extremely essential. In the context of today’s big era with a variety of English learning methods, it is required that data mining be applied to online English education. Owing to the continuous application of data mining techniques and the improvement of the online learning system, its application in education is also more and more prevalent. In the face of a large amount of learning data and student behavior data, the traditional methods have the problems of low processing efficiency, more memory requirements, and large prediction error. Therefore, this paper proposes a student behavior analysis method of online English education based on data mining. The student behavior data is collected, and an online English education learning behavior model is established. The data mining model is built to filter the obtained behavior data through data preparation, data statistics, and analysis. Furthermore, the apriori algorithm is used to mine association rules and calculate the similarity of data followed by the application of a fuzzy neural network to mine the behavior data of English online education students. The experimental results show that this method has high data processing efficiency, takes up less space, and produces a low prediction error.

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Section: Design Of Student Behavior Model For Onlinementioning

confidence: 99%

Analysis of Students’ Behavior in English Online Education Based on Data Mining

Wang

2021

Mobile Information Systems

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“…Items T 1 (11,1), (12, h 1 ), (21, h 1 ), (31, h 1 ) T 2 (11,1), (12, 1), (22, h 2 ), (31, h 2 ) T 3 (12, h 2 ), (22, h 2 ), (31, 1), (32, h 2 ) T 4 (11,1), (12, 1), (31, 1) T 5 (12,1), (21, 1), (31, 1), (32, 1) T 6 (21, h 3 ), (22, h 2 ), (31, h 2 ), (32, h 3 ) T 7 (22, h 2 ), (31, 1), (32, h 2 ) T 8 (11,1), (32, 1) T 9 (12,1), (22,1), (31, 1) T 10 (11, h 2 ), (21, h 2 ), (31, 1), (32, h 2 )…”

Section: T Idmentioning

confidence: 99%

“…Items T 1 (11,1,0), (12, 0, h 1 ) (21, 0, h 1 ), (31, 0, h 1 ) T 2 (11, 1, 0), (12, 1, 0), (22, 0, h 2 ), (31, 0, h 2 ) T 3 (12, 0, h 2 ), (22, 0, h 2 ), (31, 1, 0), (32, 0, h 2 ) T 4 (11, 1, 0), (12, 1, 0), (31, 1, 0) T 5 (12,1,0), (21, 1, 0), (31, 1, 0), (32, 1, 0) T 6 (21, 0, h 3 ), (22, 0, h 2 ), (31, 0, h 2 ), (32, 0, h 3 ) T 7…”

Section: T Idmentioning

confidence: 99%

“…As evident from the literature, the analytics act has been exercised to reveal various types of patterns such as Frequent Patterns [1][2][3][4][5], Profitable Patterns [6], Conditional Patterns [7], Calendar-Based Patterns [8], and Log Pattern Mining [9] using various techniques of pattern mining [10]. Moreover, after the success of mining knowledge from datasets, researchers deal with certain specific situations and perform various tasks such as mining on data streams [11,12], recognition of handwritten expression [13], investigating customer buying behavior through Visual Market Basket Analysis (VMBA) [14], automated assessment of shopping behavior [15,16], applying additional interestingness measures for association rule mining [17], and conditional discriminative pattern mining [18], and researchers also have to deal to improve the implementation of pattern mining algorithms using time stamp uncertainties and temporal constraints [19], privacy of frequent itemset mining using randomized response [20], and finding infrequent itemset to discover the negative association rule [21].…”

Section: Introductionmentioning

confidence: 99%

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A Model for Trend Analysis in the Online Shopping Scenario Using Multilevel Hesitation Pattern Mining

Dixit

Tiwari

Gupta

2021

Mathematical Problems in Engineering

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The present paper proposes a new model for the exploration of hesitated patterns from multiple levels of conceptual hierarchy in the transactional dataset. The usual practice of mining patterns has focused on identifying frequent patterns (i.e., which occur together) in the transactional dataset but uncovers the vital information about the patterns which are almost frequent (but not exactly frequent) called “hesitated patterns.” The proposed model uses the reduced minimum support threshold (contains two values: attractiveness and hesitation) and constant minimum confidence threshold with the top-down progressive deepening approach for generating patterns and utilizing the apriori property. To validate the model, an online purchasing scenario of books through e-commerce-based online shopping platforms such as Amazon has been considered and shown that how the various factors contributed towards building hesitation to purchase a book at the time of purchasing. The present work suggests a novel way for deriving hesitated patterns from multiple levels in the conceptual hierarchy with respect to the target dataset. Moreover, it is observed that the concepts and theories available in the existing related work Lu and Ng (2007) are only focusing on the introductory aspect of vague set theory-based hesitation association rule mining, which is not useful for handling the patterns from multiple levels of granularity, while the proposed model is complete in nature and addresses the very significant and untouched problem of mining “multilevel hesitated patterns” and is certainly useful for exploring the hesitated patterns from multiple levels of granularity based on the considered hesitation status in a transactional dataset. These hesitated patterns can be further utilized by decision makers and business analysts to build the strategy on how to increase the attraction level of such hesitated items (appeared in a particular transaction/set of transactions in a given dataset) to convert their state from hesitated to preferred items.

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“…The deficiencies of long consumption time, large redundancy probability, and large Root-Mean-Square Error of Approximation (RMSEA) are highly available in the results of data mining applications [19]. Many techniques, such as Multi-Core Algorithm for Frequent Itemsets [20], [21], Outlier detection algorithms for monitoring distance over data streams [22], were developed. However, frequent mining for uncertain data based on the extended support threshold needs more improvement.…”

Section: Introductionmentioning

confidence: 99%

Frequent Pattern Retrieval on Data Streams by using Sliding Window

Kumar¹,

Rao²

2018

EAI Endorsed Transactions on Energy Web

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In different applications like recommender frameworks and market examination, regular patterns play a significant role in useful mining data. Mining regular patterns from sliding windows over streaming information has become a complex task. In this examination, the sliding window is utilized to build the framework and FP tree applied to mine the dataset's valuable data. The sliding window has the arrangement of patterns put away in the Matrix, which contains the transaction in the sliding information and then applied to the FP tree. In this paper, the Frequent Pattern Retrieval strategy is planned by utilizing an FP tree approach and a sliding window model to extract noteworthy examples from data streams. The proposed technique accomplished less runtime with low memory use for the Breast disease dataset and different datasets to run the least utility edge contrasted with different existing procedures.

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A novel multi-core algorithm for frequent itemsets mining in data streams

Cited by 7 publications

References 16 publications

Analysis of Students’ Behavior in English Online Education Based on Data Mining

Analysis of Students’ Behavior in English Online Education Based on Data Mining

A Model for Trend Analysis in the Online Shopping Scenario Using Multilevel Hesitation Pattern Mining

Frequent Pattern Retrieval on Data Streams by using Sliding Window

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