scholarly journals Mining Frequent Itemsets Over Uncertain Database using Matrix

2020 ◽  
Vol 9 (4) ◽  
pp. 2048-2052
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Computation Complexity ◽  
Support Count ◽  
Frequent Items ◽  
Uncertain Database ◽  
Modern Era ◽  
Mining Frequent Itemsets

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.

Research into the Algorithm of Frequent Pattern Mining Based on across Linker

2012 ◽  
Vol 195-196 ◽  
pp. 984-986
Author(s):  
Ming Ru Zhao ◽  
Yuan Sun ◽  
Jian Guo ◽  
Ping Ping Dong
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Apriori Algorithm ◽  
Important Data ◽  
Classical Algorithm ◽  
Frequent Itemsets Mining ◽  
Mining Frequent Itemsets

Frequent itemsets mining is an important data mining task and a focused theme in data mining research. Apriori algorithm is one of the most important algorithm of mining frequent itemsets. However, the Apriori algorithm scans the database too many times, so its efficiency is relatively low. The paper has therefore conducted a research on the mining frequent itemsets algorithm based on a across linker. Through comparing with the classical algorithm, the improved algorithm has obvious advantages.

Research on Data Mining Based on Apriori Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1675-1679
Author(s):  
Pei Ji Wang ◽  
Yu Lin Zhao
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
Frequent Itemsets ◽  
Scientific Data ◽  
Apriori Algorithm ◽  
Data Table ◽  
Large Databases ◽  
Mining Association Rules ◽  
Mining Frequent Itemsets

With the availability of inexpensive storage and the progress in data collection tools, many organizations have created large databases of business and scientific data, which create an imminent need and great opportunities for mining interesting knowledge from data.Mining association rules is an important topic in the data mining research. In the paper, research mining frequent itemsets algorithm based on recognizable matrix and mining association rules algorithm based on improved measure system, the above method is used to mine association rules to the students’ data table under Visual FoxPro 6.0.

scholarly journals NUCLEAR: An Efficient Methods for Mining Frequent Itemsets and Generators from Closed Frequent Itemsets

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Huy Quang Pham, Duc Tran, Ninh Bao Duong, Philippe Fournier-Viger, Alioune Ngom
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Experimental Results ◽  
Extra Cost ◽  
Effective Algorithm ◽  
Closed Frequent Itemsets ◽  
Mining Frequent Itemsets

Frequent itemset (FI) mining is an interesting data mining task. Instead of directly mining the FIs from data it is preferred to mine only the closed frequent itemsets (CFIs) first and then extract the FIs for each CFI. However, some algorithms require the generators for each CFI in order to extract the FIs, leading to an extra cost. In this paper, we introduce an effective algorithm, called NUCLEAR, which can induce the FIs from the lattice of CFIs without the need of the generators. It can enumerate generators as well by similar fashion. Experimental results showed that NUCLEAR is effective as compared to previous studies, especially, the time for extracting the FIs is usually much smaller than that for mining the CFIs.

scholarly journals A Parallel Apriori Algorithm and FP- Growth Based on SPARK

2021 ◽  
Vol 40 ◽  
pp. 03046
Author(s):  
Priyanka Gupta ◽  
Vinaya Sawant
Keyword(s):  
Data Mining ◽  
Computation Time ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Distributed Data ◽  
Apriori Algorithm ◽  
Multiple Datasets ◽  
Computation Technique ◽  
Real World Applications ◽  
Spark Framework

Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets. Originally, Map-Reduce mining algorithm-based frequent itemsets on Hadoop were resolved. For handling the big data, Hadoop comes into the picture but the implementation of Hadoop does not reach the expectations for the parallel algorithm of distributed data mining because of its high I/O results in the transactional disk. According to research, Spark has an in-memory computation technique that gives faster results than Hadoop. It was mainly acceptable for parallel algorithms for handling the data. The algorithm working on multiple datasets for finding the frequent itemset to get accurate results for computation time. In this paper, we propose on parallel apriori and FP-growth algorithm to finding the frequent itemset on multiple datasets to get the mining itemsets using the Apache SPARK framework. Our experiment results depend on the support value to get accurate results.

scholarly journals Determination of Temporal Association Rules Pattern Using Apriori Algorithm

2020 ◽  
Vol 14 (2) ◽  
pp. 159
Author(s):  
Shona Chayy Bilqisth ◽  
Khabib Mustofa
Keyword(s):  
Association Rules ◽  
Business Plan ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Temporal Association ◽  
Apriori Algorithm ◽  
Time Intervals ◽  
Transaction Data ◽  
Support Count

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.

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.

scholarly journals A novel collaborative filtering algorithm by bit mining frequent itemsets

2018 ◽  
Author(s):  
Loc Nguyen ◽  
Minh-Phung T. Do
Keyword(s):  
Collaborative Filtering ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Storage Space ◽  
Model Based ◽  
Collaborative Filtering Algorithm ◽  
Speed Up ◽  
Mining Frequent Itemsets ◽  
Bitwise Operations ◽  
Process Mode

Collaborative filtering (CF) is a popular technique in recommendation study. Concretely, items which are recommended to user are determined by surveying her/his communities. There are two main CF approaches, which are memory-based and model-based. I propose a new CF model-based algorithm by mining frequent itemsets from rating database. Hence items which belong to frequent itemsets are recommended to user. My CF algorithm gives immediate response because the mining task is performed at offline process-mode. I also propose another so-called Roller algorithm for improving the process of mining frequent itemsets. Roller algorithm is implemented by heuristic assumption “The larger the support of an item is, the higher it’s likely that this item will occur in some frequent itemset”. It models upon doing white-wash task, which rolls a roller on a wall in such a way that is capable of picking frequent itemsets. Moreover I provide enhanced techniques such as bit representation, bit matching and bit mining in order to speed up recommendation process. These techniques take advantages of bitwise operations (AND, NOT) so as to reduce storage space and make algorithms run faster.

A Proposed Frequent Itemset Discovery Algorithm Based on Item Weights and Uncertainty

2020 ◽  
Vol 12 (1) ◽  
pp. 98-118
Author(s):  
Hanaa Ibrahim Abu Zahra ◽  
Shaker El-Sappagh ◽  
Tarek Ahmef El Shishtawy
Keyword(s):  
High Performance ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Real Word ◽  
Memory Consumption ◽  
Itemset Mining ◽  
Uncertain Database ◽  
Additional Value ◽  
Mining Algorithms ◽  
New Algorithms

Most frequent itemset mining algorithms (FIMA) discover hidden relationships from unrelated items. They find the most frequent itemsets depending only on the frequency of the item's existence in the dataset. These algorithms give all items the same importance, and neglect the differences in importance of the items. They assume the full certainty of data, but in most cases, real word data may be uncertain. As a result, the data could be incomplete and/or imprecise. These two problems are the most common challenges that face FIMA algorithms. Some new algorithms proposed some solutions to face these two issues separately. In other words, some algorithms handle item importance only, and others handle uncertainty only. Few algorithms dealt with the two issues together. In this article, the single scan for weighted itemsets over the uncertain database (SSU-Wfim) is proposed. It depends on the single scan frequent itemsets algorithm (SS_FIM), and enhances it to deal with weighted items in an uncertain database. SSU_WFIM deals with the uncertainty of data by giving each item in a transaction an additional value to indicate occurrence likelihood. It gives the items different values to define the weight of them. It uses a table called Ptable to save the items and their probability values. This table is used to generate all possible candidates itemsets. The results indicate the high performance in aspects of runtime, memory consumption and scalability of SSU-Wfim comparing with the UApriori algorithm. The proposed algorithm saves time and memory with a percentage exceeds 70% for all tested datasets.

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