Mining and Evaluation of Teaching Civics Courses Based on Apriori-TIDS Algorithm

The thinking course is an innovative move to implement the fundamental task of moral education and realize the whole process of education and all-round education in universities. The Apriori-TIDS algorithm proposed in this paper adopts the TID list of transaction identifiers to calculate the support count and generate the frequent item set of the Hou option set, and the whole frequency set generation process only needs to scan the transaction database once, which greatly improves the operation efficiency of the mining algorithm. The course is based on the three focus points of ideological and political education, such as “matters, times, and situations”, to explore the elements of ideological and political education hidden in the course, and to give the principles and criteria for evaluating the effectiveness of ideological and political teaching in the course, in order to make the professional degree course become the main channel to lead the ideological and political education of postgraduate courses and improve the effectiveness of the course in educating people.

IPOC: an efficient approach for dynamic association rule generation using incremental data with updating supports

According to recent statistics, there was drastic growth in online business sector where more number of customers intends to purchase items. Due to these retailers accumulates huge volumes of data from day to day operations and engrossed in analyzing the data to watch the behavior of customers at items which strengthen the business promotions and catalog management. It reveals the customer interestingness and frequent items from large data. To carry out this there was known algorithms present which deals with static and dynamic data. Some of them are lag time and memory consuming and involves unnecessary process. This paper intents to implement an efficient incremental pre ordered coded tree (IPOC) generation for data updates and applies frequent item set generation algorithm on the tree. While incremental generation of tree, new data items will link to previous nodes in tree by increasing its support count. This removes the lagging issues in existing algorithms and does not need to mine from scratch and also reduces the time, memory consumption by the use of nodeset data structure. The results of proposed method was observed and analyzed with existing methods. The anticipated method shows improved results by means of generated items, time and memory.

Elastic vibration of rotationally ring-shaped periodic structure subjected to three-axis angular velocity components

The elastic vibration of rotationally ring-shaped periodic structure (RRPS) subjected to angular velocities applied about three orthogonal directions are examined. An analytical model having in-plane radial and tangential displacements is developed by using Hamilton's principle. The modeling leads to a partial differential equation with revolution effect, based on which the eigenvalue splitting, mode contamination and vibration instability are examined by focusing on their evolutions with the support count, support strength and revolution speed. The eigensolutions are formulated by perturbation-superposition method. The results imply that the splitting, contamination and instability follow similar rules with those of stationary RRPS, which are heavily affected by the revolution speed. The dependence of parameters on eigensolutions and especially the relationships between eigenvalue splitting and principal instability, and those between mode contamination and combination instability are demonstrated based on a sample RRPS. The principal instability can occur at splitting eigenvalues, and the combination instability can arise in the presence of mode contamination. Main results are compared with the existing ones in the open literature.

Determination of Temporal Association Rules Pattern Using Apriori Algorithm

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.

Mining Frequent Itemsets Over Uncertain Database using Matrix

In the area of data mining for finding frequent itemset from huge database, there exist a lot of algorithms, out of all Apriori algorithm is the base of all algorithms. In Uapriori algorithm each items existential probability is examined with a given support count, if it is greater or equal then these items are known as frequent items, otherwise these are known as infrequent itemsets. In this paper matrix technology has been introduced over Uapriori algorithm which reduces execution time and computational complexity for finding frequent itemset from uncertain transactional database. In the modern era, volume of data is increasing exponentially and highly optimized algorithm is needed for processing such a large amount of data in less time. The proposed algorithm can be used in the field of data mining for retrieving frequent itemset from a large volume of database by taking very less computation complexity.

Acute Kidney Injury is Associated with Poor Lung Outcomes in Infants Born ≥32 Weeks of Gestational Age

Objective This study aimed to evaluate the association between acute kidney injury (AKI) and lung outcomes in infants born ≥32 weeks of gestational age (GA). Study Design Secondary analysis of infants ≥32 weeks of GA in the assessment of worldwide acute kidney injury epidemiology in neonates (AWAKEN) retrospective cohort (n = 1,348). We used logistic regression to assess association between AKI and a composite outcome of chronic lung disease (CLD) or death at 28 days of age and linear regression to evaluate association between AKI and duration of respiratory support. Results CLD occurred in 82/1,348 (6.1%) infants, while death occurred in 22/1,348 (1.6%); the composite of CLD/death occurred in 104/1,348 (7.7%). Infants with AKI had an almost five-fold increased odds of CLD/death, which remained after controlling for GA, maternal polyhydramnios, multiple gestations, 5-minute Apgar's score, intubation, and hypoxic–ischemic encephalopathy (adjusted odds ratio [OR] = 4.9, 95% confidence interval [CI]: 3.2–7.4; p < 0.0001). Infants with AKI required longer duration of respiratory support (count ratio = 1.59, 95% CI: 1.14–2.23, p = 0.003) and oxygen (count ratio = 1.43, 95% CI: 1.22–1.68, p < 0.0001) compared with those without AKI. Conclusion AKI is associated with CLD/death and longer duration of respiratory support in infants born at ≥32 weeks of GA. Further prospective studies are needed to elucidate the pathophysiologic relationship.

Improved Frequent Item-Sets Mining in Pharmacovigilance

Mining frequent item-sets is an important concept that deals with fundamental and initial task of data mining. Apriori is the most popular and frequently used algorithm for finding frequent item-sets which is preferred over other algorithms like FP-growth due to its simplicity. For improving the time efficiency of Apriori algorithms, Jiemin Zheng introduced Bit-Apriori algorithm with the enhancement of support count and special equal support pruning with respect to Apriori algorithm. In this paper, a novel Bit-Apriori algorithm, that deletes infrequent items during trie2 and subsequent tries are proposed which can be used in pharmacovigilance to identify the adverse event

Pola peminjaman buku di perpustakaan Universitas Syiah Kuala menggunakan Algoritma Eclat

Introduction. UPT Unsyiah Library is one of the facilities in Syiah Kuala University which provides book lending service to users.The library collects all information and has expanded a big data of book lending.Data Collection Method. This research aims to determine the relevance pattern between the book subject and the borrower's program of study, and to determine the pattern of book borrowing based on books that are often borrowed simultaneously. The pattern can be found using one of the methods of data mining that is the association rules mining with Eclat algorithm. Eclat algorithm uses vertical format of dataset to intersect TID list between items in determining support count so that the process of searching frequent itemset is faster.Analysis Data. There are 122.945 book lending data from 2007 to 2015 used in this study. These data show the borrowers’ behavior pattern of book lending behavior in UPT Library Unsyiah, especially the borrowers who are student of this university. Results and Discussions. The Eclat algorithm produces the most frequent and repeatable pattern of book subjects and program of studies from several years of research data, which are Accounting book subjects with its program of study (S1) and Chemistry book subjects with Chemistry Education program of study (S1).Conclusions. The analysis result for the book subject pattern and program of studies shows that the habit of Unsyiah students in borrowing books from the library is accordingly to their program of studies. As for the patterns between books, Eclat algorithm found linkage between books and most often repeated from several periods of years of research data is the book code of 12311 (Fundamentals of educational evaluation) with 42265 (Introduction to evaluation of education).

Coalesce based binary table: an enhanced algorithm for mining frequent patterns

Frequent item set mining and association rule mining is the key tasks in knowledge discovery process. Various customized algorithms are being implemented in Association Rule Mining process to find the set of frequent patterns. Though we have many algorithms apriori is one of the standard algorithm for finding frequent itemsets, but this algorithm is inefficient because of several scans of database and more number of candidates to be generated. To overcome these limitations, in this paper a new algorithm called Coalesce based Binary Table is introduced. Through this algorithm the given database is scanned only once to generate Binary Table by which frequent-1 itemsets are found. To progress the process, infrequent-1 itemsets are identified and removed from the Binary Table to rearrange the items in support ascending order. To each frequent-1 itemset find Coalesce matrix and Index List to generate all frequent itemsets having the same support count as representative items and the remaining frequent itemsets are obtained in depth first manner. The significant benefits with the proposed method are the whole database is scanned only once, no need to generate and check each candidate to find the set of frequent items. On the other hand frequent items having the same support counts as representative items can be identified directly by joining the representative item with all the combinations of Coalesce matrix. So, it is proven that coalesce based Binary Table is panacea to cut short the time in identifying the frequent itemsets hence the efficiency is improved.