An Improved Apriori Algorithm for Association Mining Between Physical Fitness Indices of College Students

Abstract

The physical fitness of college students can be evaluated scientifically based on the data of physical education (PE). This paper firstly relies on the Apriori algorithm to mine the hidden correlations between the physical fitness indices from the PE data on college students, and identify the indices closely associated with the physical fitness of college students. Then, the Apriori algorithm was improved to reduce the time complexity of association rule mining. Based on the improved algorithm, it was learned that the correlation coefficients of several indices surpassed the minimum support of 0.2 and minimum confidence of 0.7, reflecting their important impacts on physical fitness. Thus, physical fitness of college students is significantly influenced by speed, endurance, flexibility, and vital capacity, but not greatly affected by height and weight. The research results provide an important guide for the test and curriculum designs of PE for college students.

Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

“…Liu N et al in the study of College English score analysis, the College English test system software based on data mining mainly realizes the automatic generation of test papers by computer programs, sets the test time, automatically judges the test scores of candidates, and gives the scores on the spot [10]. Pan T uses Apriori algorithm to mine the hidden correlation between physical fitness indicators from college students' sports data and identify the indicators closely related to college students' physical fitness [11]. In the analysis of students' psychological problems, Liu J and others used data mining technology to realize the dynamic management of psychological early warning data, monitor the psychology of high-risk groups in real time, and improve the accuracy and effectiveness of early identification and early warning of students' psycho-logical crisis [12].…”

Identification of factors affecting noise levels by using data mining and design of experiments analysis techniques: A novel experimental approach

“…26–28 Also, the physical fitness of college students can be analyzed. 30 Moreover, supermarket layout research can also be studied. 31 Below, the pseudo-code of the Apriori algorithm is displayed:

L1={large 1−itemsets}; for (k=2; Lk−1≠0;k++); Ck=apriori−gen(Lk−1); for all transactions t∈D do begin Ct=subset(Ck,t); for all candidates c∈Ct do c .count ++; end Lk={c∈Ck|c.count≥minsup

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“…[26][27][28] Also, the physical fitness of college students can be analyzed. 30 Moreover, supermarket layout research can also be studied. 31 Below, the pseudo-code of the Apriori algorithm is displayed: Here, "L" and "C" are defined as frequent itemsets and candidates, respectively.…”

Digital innovations in higher education management and mechanisms for high-quality training of students

“…Finding frequent item sets means extracting the combination of transactions that satisfy the threshold condition from the transaction set based on the relative frequency of a group of items appearing in the transaction pool individually or jointly, and this step is the key part of the algorithm. Mining association rules are based on finding frequent itemsets to mine transactions that may have strong relationships based on specific decision conditions [22][23].…”