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Lawrance, R.
- A Frequent Pattern Tree Algorithm for Mining Association Rule Using Genetic Algorithm
Abstract Views :210 |
PDF Views:2
Authors
K. Poornamala
1,
R. Lawrance
2
Affiliations
1 Computer Science Department, Ayya Nadar Jankai Ammal College, Sivakasi, IN
2 Computer Application Department, Ayya Nadar Janaki Ammal College, Sivakasi, IN
1 Computer Science Department, Ayya Nadar Jankai Ammal College, Sivakasi, IN
2 Computer Application Department, Ayya Nadar Janaki Ammal College, Sivakasi, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 357-360Abstract
In recent years, data mining is an important aspect for generating association rules among the large number of itemsets. Association rule mining is one of the techniques in data mining that has two subprocess. First, the process called as finding frequent itemsets and second process is association rules mining. In this subprocess, the rules with the use of frequent itemsets have been extracted. Researchers developed a lot of algorithms for finding frequent itemsets and association rules. The frequent pattern technique only used for very large dataset and it takes large memory space tree creation. The major advantage of using Genetic Algorithm is that it perform global search and the time complexity is less compared to other algorithms. In this paper, first, GA is used to optimize the large dataset. Second, the improved frequent pattern tree is used to mine the frequent itemset without generating conditional FP-tree.Keywords
Association Rule Mining, Data Mining, Frequent Itemset Mining, FP-Tree, Genetic Algorithm.
- Boolean Algebraic Algorithm for Mining Association Rules from Large Database
Abstract Views :182 |
PDF Views:3
Authors
S. Sumathi
1,
R. Lawrance
2
Affiliations
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Ayya Nadar Janaki Ammal College, Sivakasi, IN
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Ayya Nadar Janaki Ammal College, Sivakasi, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 361-364Abstract
In the earlier days, the association rule mining is used for Market Basket analysis to find the regularity in purchasing behavior of customer. Association Rule Mining (ARM) is one of the functionalities in Data Mining, to find the relationships among the items in a particular set of itemsets. There are huge numbers of algorithms to find relationships among the items. In this paper we introduce a new Boolean algebraic algorithm for finding frequent itemsets and deriving the association rules in a large transaction database. It has two phases. In the first phase, it finds the frequent itemsets. In the second phase, by using the Boolean AND and XOR operator, it derives the association rules from the founded frequent itemset in first phase. This algorithm mines the association rules efficiently than Apriori.Keywords
Association Rule Mining, Boolean Algebra, Data Mining, Frequent Item Set Mining.- Mining Fuzzy Frequent Item Set Using Compact Frequent Pattern (CFP) Tree Algorithm
Abstract Views :231 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi-626124, Tamil Nadu, IN
2 Department of MCA, Ayya Nadar Janaki Ammal College, Sivakasi-626124, Tamil Nadu, IN
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi-626124, Tamil Nadu, IN
2 Department of MCA, Ayya Nadar Janaki Ammal College, Sivakasi-626124, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 365-369Abstract
The problem of mining quantitative data from large transaction database is considered to be an important critical task. Researchers have proposed efficient algorithms for mining of frequent itemsets based on Frequent Pattern (FP) tree like structure which outperforms Apriori like algorithms by its compact structure and less generation of candidate itemsets mostly for binary data items from huge transaction database. Fuzzy logic softens the effect of sharp boundary intervals and solves the problem of uncertainty present in data relationships. This proposed approach integrates the fuzzy logic in the newly invented tree-based algorithm by constructing a compact sub-tree for a fuzzy frequent item significantly efficient than other algorithms in terms of execution times, memory usages and reducing the search space resulting in the discovery of fuzzy frequent itemsets.Keywords
Association Rule Mining, Data Mining, Fuzzy Frequent Itemset, Fuzzy Logic, Membership Function.- A New Hybrid Search Based Algorithm Using Partition-COFI Tree in Association Rule Mining
Abstract Views :242 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Ayya Nadar Janaki Ammal College, Sivakasi, IN
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Ayya Nadar Janaki Ammal College, Sivakasi, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 370-373Abstract
In recent years, most of the researchers discover the association rules among itemsets for large database become popular. It is one of the techniques used to mine the database. There are several efficient algorithms are produced different search strategies for finding the frequent itemsets and those algorithms are very popular in the association rule mining. Many association rule mining algorithms suffer from many problems when mining the massive datasets. Some of the major problems are: (1) repetitive scans (2) huge computation time takes during the candidacy generation and (3) high memory space required. This paper, proposed a hybrid search algorithm are developed for mining multilevel association rules and it improve the performance of algorithm. This algorithm is named as Partition-COFI Tree i.e., PC tree. The proposed algorithm works faster compared to other algorithm. It improves the performance of search space, I/O and CPU time.Keywords
Association Rule Mining, Cofi Tree, Data Mining, Partition Algorithm.- An Analysis of Teachers’ Performance Using Decision Tree Based C5.0 Mapreduce Algorithm Using Bigdata Mining
Abstract Views :170 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi, IN
1 Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi, IN
Source
Data Mining and Knowledge Engineering, Vol 10, No 4 (2018), Pagination: 78-83Abstract
Data mining is one of the potential research fields regarding interdisciplinary aspects. Educational data mining is developing discipline in the present scenario. Classification techniques in the data mining plays an important role in the area of educational data mining. The main goal regarding this work is to predict the teachers’ performance by using the relevant features. The proposed methodology consists of the phases like preprocessing, attribute selection, classification based on decision tree and performance evaluation. In the data preprocessing phase, the missing values have been filluped. The attributes are converted into a categorized format using the categorization MapReduce process. The gain ratio with MapReduce is the best method for feature selection, since it selects technique extracted the relevant attributes in an accurate manner, which takes less time compares to the other feature selection methods. This paper presents a MapReduce algorithm on the classification structure by using C5.0 algorithm, aiming to accumulate time and obtain high accuracy on huge students’ and teachers’ datasets. The classification process based on C5.0 MapReduce algorithm is resulted with good classification accuracy.
Keywords
Educational Data Mining, Students’ and Teachers’ Dataset, MapReduce, Classification, C5.0 Algorithm, Big Data Mining.References
- Bakar, R.S., and Yacef, K, “The state of educational data mining in 2009: A review and future visions.” JEDM-Journal of Educational Data Mining 1.1 (2009):3-17
- Barracosa, J.I.M.S.2011. Mining Behaviors from Educational Data
- http://library.queensu.ca/webedu/grad/Purpose_of_the_Literature_ Review.pdf
- Lawrance, R., Shanmugarajeshwari, V., “Analysis of Students’ Performance Evaluation using Classification Techniques.” IEEE International Conference on Computing Technologies and Intelligent Data Engineering(ICCTIDE’16), 10.1109/ICCTIDE.2016.7725375, 16426971
- Agaoglu, M., IEEE Translation and Content Mining, “Predicting Instructor Performance Using Data Mining Techniques in HigherEducation.” Department of Computer Engineering, Marmara University, Istanbul 334722, Turkey. Volume.4, 2016, pp:2379-2387
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