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.

Data Stream Frequent Closed Item Sets Mining Based on Fast Sliding Window

2011 ◽  
Vol 130-134 ◽  
pp. 3702-3707
Author(s):  
Zhi Hua Chen ◽  
Jun Luo
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Screening Method ◽  
Hash Table ◽  
Sliding Window ◽  
Threshold Value ◽  
Space Efficiency ◽  
Frequent Item ◽  
Support Threshold ◽  
Frequent Item Sets

According to the mobility and continuity of the flow of data streams，this paper presents an algorithm called NSWR to mine the frequent item sets from a fast sliding window over data streams and it meets people’s needs of getting the frequent item sets over data that recently arrive. NWSR, using an effective bit-sequence representation of items based on the data stream sliding window, helps to store data; to support different support threshold value inquiry through hash-table-based frequent closed item sets results query method; to offer screening method based on the classification of closed item sets for reducing the number of item sets that need closure judgments, effectively reducing the computational complexity. Experiments show that the algorithm has better time and space efficiency.

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.

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.

Cross Sellingusing Association Rule Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1337-1341
Author(s):  
Ran Bo Yao ◽  
An Ping Song ◽  
Xue Hai Ding ◽  
Ming Bo Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
New Method ◽  
Experimental Result ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Direct Benefits ◽  
Frequent Item Sets

In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

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