scholarly journals Improved Frequent Item-Sets Mining in Pharmacovigilance

2019 ◽  
Vol 8 (2S4) ◽  
pp. 288-291
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Apriori Algorithm ◽  
Time Efficiency ◽  
Important Concept ◽  
Frequent Item ◽  
Initial Task ◽  
Support Count ◽  
Frequent Item Sets

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

scholarly journals Extrication of Apriori Algorithm using Association Rules on Medical Data sets

2019 ◽  
pp. 107-112
Author(s):  
Anusha Viswanadapalli ◽  
Praveen Kumar Nelapati
Keyword(s):  
Data Mining ◽  
Research Study ◽  
Medical Data ◽  
Frequent Pattern ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Compact Structure ◽  
Frequent Item ◽  
Frequent Pattern Tree ◽  
Frequent Item Sets

During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The APRIORI growth algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or APRIORI tree), and frequent item set is mining by using of APRIORI tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (APRIORI-Growth, COFI-Tree, CT-PRO) based upon the APRIORI- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on MEDICAL data to generate rules and patterns using Frequent Pattern (APRIORI)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on MEDICAL data.

The Research of Improved Apriori Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2179-2184 ◽  
Author(s):  
Zhen Yun Liao ◽  
Xiu Fen Fu ◽  
Ya Guang Wang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Mining Algorithm ◽  
The Times ◽  
Frequent Item Sets ◽  
Candidate Item ◽  
Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

Security and Verification of Server Data Using Frequent Itemset Mining in Ecommerce

2017 ◽  
Vol 8 (1) ◽  
pp. 31-43
Author(s):  
Zuber Shaikh ◽  
Antara Mohadikar ◽  
Rachana Nayak ◽  
Rohith Padamadan
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Graphical Password ◽  
Itemset Mining ◽  
Frequent Item ◽  
Data Mining Algorithms ◽  
Shoulder Surfing ◽  
Mining Algorithms ◽  
Frequent Item Sets

Frequent itemsets refer to a set of data values (e.g., product items) whose number of co-occurrences exceeds a given threshold. The challenge is that the design of proofs and verification objects has to be customized for different data mining algorithms. Intended method will implement a basic idea of completeness verification and authentication approach in which the client will uses a set of frequent item sets as the evidence, and checks whether the server has missed any frequent item set as evidence in its returned result. It will help client detect untrusted server and system will become much more efficiency by reducing time. In authentication process CaRP is both a captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks.

AFARTICA

2019 ◽  
Vol 30 (3) ◽  
pp. 71-93
Author(s):  
Saubhik Paladhi ◽  
Sankhadeep Chatterjee ◽  
Takaaki Goto ◽  
Soumya Sen
Keyword(s):  
Threshold Value ◽  
Search Space ◽  
Apriori Algorithm ◽  
The Novel ◽  
Novel Technique ◽  
Frequent Item ◽  
Artificial Cell ◽  
Typical Item ◽  
Support Threshold ◽  
Frequent Item Sets

Frequent item-set mining has been exhaustively studied in the last decade. Several successful approaches have been made to identify the maximal frequent item-sets from a set of typical item-sets. The present work has introduced a novel pruning mechanism which has proved itself to be significant time efficient. The novel technique is based on the Artificial Cell Division (ACD) algorithm which has been found to be highly successful in solving tasks that involve a multi-way search of the search space. The necessity conditions of the ACD process have been modified accordingly to tackle the pruning procedure. The proposed algorithm has been compared with the apriori algorithm implemented in WEKA. Accurate experimental evaluation has been conducted and the experimental results have proved the superiority of AFARTICA over apriori algorithm. The results have also indicated that the proposed algorithm can lead to better performance when the support threshold value is more for the same set of item-sets.

scholarly journals Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

2019 ◽  
Vol 9 (18) ◽  
pp. 3819 ◽  
Author(s):  
Wenhao Guo ◽  
Xiaoqing Zuo ◽  
Jianwei Yu ◽  
Baoding Zhou
Keyword(s):  
Prediction Model ◽  
Early Warning ◽  
Computational Efficiency ◽  
Prevention And Control ◽  
Apriori Algorithm ◽  
Mapreduce Framework ◽  
Frequent Item ◽  
And Control ◽  
Frequent Item Sets

In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.

scholarly journals Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

2019 ◽  
Vol 19 (3) ◽  
pp. 154-167 ◽  
Author(s):  
Indah Werdiningsih ◽  
Rimuljo Hendradi ◽  
Barry Nuqoba ◽  
Elly Ana ◽  
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Association Rules ◽  
Principal Component ◽  
Feature Reduction ◽  
Apriori Algorithm ◽  
Childhood Diseases ◽  
Frequent Item ◽  
Computing Performance ◽  
Frequent Item Sets

Abstract This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

Algorithm of Frequent Item Sets Mining Based on Index Table

2013 ◽  
Vol 373-375 ◽  
pp. 1076-1079
Author(s):  
Lin Zhang ◽  
Nan Zhen Yao ◽  
Jian Li Zhang
Keyword(s):  
Low Cost ◽  
Apriori Algorithm ◽  
Two Dimensional ◽  
One Dimensional ◽  
Single Index ◽  
Frequent Item ◽  
The One ◽  
Index Value ◽  
Frequent Item Sets ◽  
Index Table

The paper gave a new frequent item sets mining algorithm based on index table at multiple times for the Apriori algorithm scans the database which causes the I/O load is too large, and the costly problem with the Apriori algorithm which want to have a big candidate sets. The algorithm first generated a one-dimensional index table by scan the database once, and then generates a two-dimensional index table based on the one-dimensional index table. After the two-dimension index table had been generated, we can use the method similar with Floyd algorithm, which inserts the single index entry individually into the two-dimensional index table. If the count of new index value is greater than or equal to Minsuppor after the single index item had been inserted, the new index entrys Item will be a frequently item sets. After all single index entry had been inserted into the two-dimensional index table, all the index entry in the table will be the maximum frequently item sets. After analysis we can see that this algorithm has low cost and with the high accuracy than Apriori algorithm and can provide some reference for related rules.

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