The Novel Model of Construct Materials Science and Information Based on Association Rule Mining

2013 ◽  
Vol 327 ◽  
pp. 197-200
Author(s):  
Guo Fang Kuang ◽  
Ying Cun Cao
Keyword(s):  
Experimental Data ◽  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Materials Science ◽  
Frequent Itemsets ◽  
Data Sets ◽  
The Novel ◽  
Rule Mining ◽  
Novel Model

The material is used by humans to manufacture the machines, components, devices and other products of substances. Association rules originated in the field of data mining, people use it to find large amounts of data between itemsets of the association. Apriori is a breadth-first algorithm to obtain the support is greater than the minimum support of frequent itemsets by repeatedly scanning the database. This paper presents the construction of materials science and information model based on association rule mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

Research on Association Rule Mining Algorithm Based on Distributed Data

2014 ◽  
Vol 998-999 ◽  
pp. 899-902 ◽  
Author(s):  
Cheng Luo ◽  
Ying Chen
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Frequent Itemsets ◽  
Network Communication ◽  
Data Mining Algorithm ◽  
Distributed Data ◽  
Rule Mining ◽  
Mining Algorithm

Existing data miming algorithms have mostly implemented data mining under centralized environment, but the large-scale database exists in the distributed form. According to the existing problem of the distributed data mining algorithm FDM and its improved algorithms, which exist the problem that the frequent itemsets are lost and network communication cost too much. This paper proposes a association rule mining algorithm based on distributed data (ARADD). The mapping marks the array mechanism is included in the ARADD algorithm, which can not only keep the integrity of the frequent itemsets, but also reduces the cost of network communication. The efficiency of algorithm is proved in the experiment.

scholarly journals AN OPTIMIZED ARM SCHEME FOR DISTINCT NETWORK DATA SET

2015 ◽  
pp. 191-195
Author(s):  
K.GANESH KUMAR ◽  
H.VIGNESH RAMAMOORTHY ◽  
M.PREM KUMAR ◽  
S. SUDHA
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
Research Area ◽  
Sequential Algorithm ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Communication Costs

Association rule mining (ARM) discovers correlations between different item sets in a transaction database. It provides important knowledge in business for decision makers. Association rule mining is an active data mining research area and most ARM algorithms cater to a centralized environment. Centralized data mining to discover useful patterns in distributed databases isn't always feasible because merging data sets from different sites incurs huge network communication costs. In this paper, an improved algorithm based on good performance level for data mining is being proposed. In local sites, it runs the application based on the improved LMatrix algorithm, which is used to calculate local support counts. Local Site also finds a center site to manage every message exchanged to obtain all globally frequent item sets. It also reduces the time of scan of partition database by using LMatrix which increases the performance of the algorithm. Therefore, the research is to develop a distributed algorithm for geographically distributed data sets that reduces communication costs, superior running efficiency, and stronger scalability than direct application of a sequential algorithm in distributed databases.

scholarly journals An Enhanced Approach to Mine Maximal Frequent Itemset using Maximal Frequent Itemset Prima Algorithm (MFIPA)

2019 ◽  
Vol 8 (S2) ◽  
pp. 9-12
Author(s):  
R. Smeeta Mary ◽  
K. Perumal
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Frequent Itemset ◽  
Decision Makers ◽  
New Method ◽  
Rule Mining

In data mining finding out the frequent itemsets is one of the very essential topics. Data mining helps in identifying the best knowledge for different decision makers. Frequent itemset generation is the precondition and most time-consuming method for association rule mining. In this paper we suggest a new algorithm for frequent itemset detection that works with datasets in distributed manner. The proposed algorithm brings in a new method to find frequent itemset not including the necessitate to create candidate itemsets. The proposed approach could be implemented using horizontal representation for transaction datasets and allocating prime value. It explores all the frequent itemset that is present in the input and according to the support the maximum frequent itemset is identified. It was applied on different transactions database and compared with well-known algorithms: FP-Growth and Parallel Apriori with different support levels. The try out showed that the proposed algorithm attain major time improvement over both algorithms.

Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix

2014 ◽  
Vol 571-572 ◽  
pp. 57-62
Author(s):  
Si Hui Shu ◽  
Zi Zhi Lin
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recursive Algorithm ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Temporal Complexity ◽  
Data Mining Algorithms ◽  
Association Data ◽  
Mining Algorithms

Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

scholarly journals An improvement of FP-Growth association rule mining algorithm based on adjacency table

2018 ◽  
Vol 189 ◽  
pp. 10012 ◽  
Author(s):  
Ming Yin ◽  
Wenjie Wang ◽  
Yang Liu ◽  
Dan Jiang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Hash Table ◽  
Frequent Itemsets ◽  
Frequent Pattern ◽  
Data Sets ◽  
Rule Mining ◽  
Mining Algorithm ◽  
Mining Frequent Itemsets ◽  
Improved Algorithm

FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.

scholarly journals Data Mining Approach in Retail Knowledge Discovery and Internet Technologies

2018 ◽  
Vol 7 (2) ◽  
pp. 100-105
Author(s):  
Simranjit Kaur ◽  
Seema Baghla
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Data ◽  
Internet Technology ◽  
Categorical Variables ◽  
Data Sets ◽  
Rule Mining ◽  
Data Set ◽  
Demographic Profiles

Online shopping has a shopping channel or purchasing various items through online medium. Data mining is defined as a process used to extract usable data from a larger set of any raw data. The data set extraction from the demographic profiles and Questionnaire to investigate the gathered based by association. The method for shopping was totally changed with the happening to internet Technology. Association rule mining is one of the important problems of data mining has been used here. The goal of the association rule mining is to detect relationships or associations between specific values of categorical variables in large data sets.

scholarly journals Association Rule Mining on Spambase Dataset using Tanagra

2020 ◽  
Vol 9 (6) ◽  
pp. 890-894
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Huge Amount ◽  
Memory Space ◽  
Better Than

There is huge amount of data being generated every minute on internet. This data is of no use until we cannot extract useful information from it. Data mining is the process of extracting useful information or knowledge from this huge amount of data that can be further used for various purposes. Discovering Association rules is one of the most important tasks among all other data mining tasks. Association rules contain the rules in the form of IF then THAN form. The leftmost part of the rule i.e. IF is called as the Antecedent which defines the condition and the rightmost part i.e. ELSE is called as the Consequent which defines the result. In this paper, we present the overview and comparison of Apriori, Apriori PT and Frequent Itemsets algorithm of association component in Tanagra Tool. We analyzed the performance based on the execution time and memory used for different number of instances, support and Rule Length in Spambase Dataset. The results show that when we increase the support value the Apriori PT takes the less execution time and Apriori takes less memory space. When numbers of instances are reduced Frequent Itemsets outperforms well both in case of memory and execution time. When rule length is increased the Apriori algorithm performs better than Apriori PT and Frequent Itemsets.

Flexible Mining of Association Rules

2011 ◽  
pp. 890-894
Author(s):  
Hong Shen
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Strong Association ◽  
Frequent Itemsets ◽  
Efficient Algorithms ◽  
Multiple Level ◽  
Rule Mining ◽  
Unique Identifier

The discovery of association rules showing conditions of data co-occurrence has attracted the most attention in data mining. An example of an association rule is the rule “the customer who bought bread and butter also bought milk,” expressed by T(bread; butter)? T(milk). Let I ={x1,x2,…,xm} be a set of (data) items, called the domain; let D be a collection of records (transactions), where each record, T, has a unique identifier and contains a subset of items in I. We define itemset to be a set of items drawn from I and denote an itemset containing k items to be k-itemset. The support of itemset X, denoted by Ã(X/D), is the ratio of the number of records (in D) containing X to the total number of records in D. An association rule is an implication rule ?Y, where X; ? I and X ?Y=0. The confidence of ? Y is the ratio of s(?Y/D) to s(X/D), indicating that the percentage of those containing X also contain Y. Based on the user-specified minimum support (minsup) and confidence (minconf), the following statements are true: An itemset X is frequent if s(X/D)> minsup, and an association rule ? XY is strong i ?XY is frequent and ( / ) ( / ) X Y D X Y ? ¸ minconf. The problem of mining association rules is to find all strong association rules, which can be divided into two subproblems: 1. Find all the frequent itemsets. 2. Generate all strong rules from all frequent itemsets. Because the second subproblem is relatively straightforward ? we can solve it by extracting every subset from an itemset and examining the ratio of its support; most of the previous studies (Agrawal, Imielinski, & Swami, 1993; Agrawal, Mannila, Srikant, Toivonen, & Verkamo, 1996; Park, Chen, & Yu, 1995; Savasere, Omiecinski, & Navathe, 1995) emphasized on developing efficient algorithms for the first subproblem. This article introduces two important techniques for association rule mining: (a) finding N most frequent itemsets and (b) mining multiple-level association rules.

Mining Frequent Itemsets Algorithm Based on Compression Matrix

2014 ◽  
Vol 556-562 ◽  
pp. 3501-3505
Author(s):  
Zi Zhi Lin ◽  
Si Hui Shu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Spatial Complexity ◽  
Temporal Complexity ◽  
New Strategy ◽  
Mining Frequent Itemsets

Association rule mining is one of the most important and well researched techniques of data mining. The key procedure of the association rule mining is to find frequent itemsets. In this paper, a new mining frequent itemsets algorithm based on matrix is introduced. Frequent itemsets are obtained by compressing the transaction matrix efficiently by a new strategy. The new algorithm optimizes the known mining frequent itemsets algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity. It is more feasible especially when the degrees of the frequent itemsets are high.

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