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Thangaraj, M.
- Discovering Frequent Access Patterns in a Digital Library Using Association Mining
Abstract Views :284 |
PDF Views:3
Authors
Affiliations
1 Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, IN
1 Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, IN
Source
Journal of Information and Knowledge (Formerly SRELS Journal of Information Management), Vol 42, No 2 (2005), Pagination: 131-138Abstract
Data Mining, also known as knowledge discovery in databases, has been recognized as a promising new area for database research. Mining frequent item sets in transactional databases, binary transaction tables, time series databases and many other kinds of databases have been an active research topic over the past few years. Frequent access pattern is a special case of sequential pattern in an application database which helps to make effective decisions in the respective problem domain.
Given a large database of book transactions in the library, where each transaction consists of book-id, name of the book, author, and other related fields, the problem is to mine the frequent access patterns of the user from the library databases. The outcome of the findings will help the management to take effective steps that will cater the needs of the user.
Apriori and FP-growth algorithms can mine the complete sets of frequent item sets. These two algorithms were implemented and the performance of the algorithms was studied. The result shows that FP-growth algorithm performs well compared to Apriori.
Keywords
Digital Library, Access Patterns, Apriori, FP-Growth, Algorithm, Mining.
References
- Srikant (R); Agrawal. (R). Mining Sequential Patterns: Generalizations and Performance Improvements. Research Report RJ 9994, IBM Almaden Research Center, San Jose, California, December 1995.
- Mining Association Rules with Item Constraints. IBM Almaden Research Centre, San Jose, USA.
- Helen Pinto; Jiawei Han; Jian Pei; Ke Wang. Multi-dimensional Sequential Pattern Mining, Work Report, Intelligent Database Systems Research Lab, School of Computing Science, Simon Fraser University, Canada.
- Mobasher (B); Cooley (R); Srivastava (J). Automatic Personalization based on Web Usage Mining. In Communications of the ACM. (43) 8, Aug. 2000.
- Agrawal (R); Srikant (R). Mining Sequential Patterns. Research Report RJ 9910, IBM Almaden Research Centre, San Jose, California, October 1994.
- Jiawei Han; Micheline Kamber. Data Mining - Concepts and Techniques. Morgan Kaufmann Publishers, 2001.
- Agricultural Labourers Social Security and Welfare Scheme in Tamil Nadu
Abstract Views :394 |
PDF Views:1
Authors
Affiliations
1 Madras Institute of Development Studies, Chennai, IN
1 Madras Institute of Development Studies, Chennai, IN
Source
Artha Vijnana: Journal of The Gokhale Institute of Politics and Economics, Vol 43, No 1-2 (2001), Pagination: 233-245Abstract
The government of Tamil Nadu constituted a Committee on Agricultural Labourers in 1997 to analyze the nature extent of socio-economic problems faced by the agricultural labourers in Tamil Nadu and to suggest the ways and means of improving their levels of living. The Committee was assisted by the representatives of the trade union of agricultural labourers, members of the farmers association and academians . The report was submitted to the government of Tamil Nadu in 1998. The Committee has made several recommendations for the overall development of the agricultural labourers. The report was well received by the agricultural labourers' trade union andd NGOs and they demanded that the government should accept the recommendations of the committee's report. They also conducted several struggles for implementation of the committee's report. The government of Tamil Nadu issued an order in 2001 for the establishment of the "Tamil Nadu agricultural labourers social security tutd welfare scheme". The immediate cause for the estublishment of this welfare board is due to the fact tha1 the agricultural labourers trade unions and NGOs were fighting for the implementation of the recommendations of the Kolappon Committee report on agricultural labourers. The objectives of the paprr are: I) to examine the report on the committee on agricultural labourers and 2) to analyse the nature and the functions of the agricultural labourers social security and welfare scheme.- Mining the Contact Lens Adhering Bacteria through Machine Learning and Clinical Analysis
Abstract Views :140 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Madurai Kamaraj University, Madurai - 625021, Tamil Nadu, IN
2 Department of CA and IT, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
3 Department of Zoology and Microbiology, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
1 Department of Computer Science, Madurai Kamaraj University, Madurai - 625021, Tamil Nadu, IN
2 Department of CA and IT, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
3 Department of Zoology and Microbiology, Thiagarajar College, Madurai - 625009, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 28 (2016), Pagination:Abstract
Objectives: Even when studies report most of the Contact Lens (CLs) wearers possess improved vision, there are some potential risks with the development of microbial keratitis. This is in turn creates research issue under public health concern. Methods/Analysis: The methodology of the work determines the culture sensitivity of the recovered isolates from three different CLs users: Daily disposable lens, monthly disposable lens and yearly disposable lens. Findings: Through the machine learning tool called Waikato Environment for Knowledge Analysis (WEKA) and extensive clinical laboratory analysis, the study provides information on prevalent Contact Lens adhering bacteria involved in causing keratitis and examine microbial biofilm formation using Scanning Electron Microscopic (SEM) analysis. The sample type of the lens with the bacterial infections were then statistically analyzed, so that the knowledge mined would aid the medical practitioners in the treatment of bacterial keratitis. Novelty/Improvement: The present study supports the treatment of bacterial keratitis associated with Contact Lens users to reduce or to prevent the adverse effects caused by bacterial pathogens.Keywords
Bacteria, Clinical, Contact Lens, Keratitis, Knowledge.Full Text
- Classification Algorithms with Attribute Selection:An Evaluation Study using WEKA
Abstract Views :207 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Raja Dorai Singam Govt Arts College, Sivagangai, IN
2 Department of Computer Science, Madurai kamaraj University, Madurai, IN
3 Department of CA & IT, Thiagarajar College, Madurai, IN
4 Department of CA, NIT, Tiruchi, IN
1 Department of Computer Science, Raja Dorai Singam Govt Arts College, Sivagangai, IN
2 Department of Computer Science, Madurai kamaraj University, Madurai, IN
3 Department of CA & IT, Thiagarajar College, Madurai, IN
4 Department of CA, NIT, Tiruchi, IN
Source
International Journal of Advanced Networking and Applications, Vol 9, No 6 (2018), Pagination: 3640-3644Abstract
Attribute or feature selection plays an important role in the process of data mining. In general the dataset contains more number of attributes. But in the process of effective classification not all attributes are relevant. Attribute selection is a technique used to extract the ranking of attributes. Therefore, this paper presents a comparative evaluation study of classification algorithms before and after attribute selection using Waikato Environment for Knowledge Analysis (WEKA). The evaluation study concludes that the performance metrics of the classification algorithm, improves after performing attribute selection. This will reduce the work of processing irrelevant attributes.Keywords
Attribute Filters, Attribute Selection, Classification, Data Mining, Weka.Full Text
References
- Meenatchi V.T, Gnanambal S, et.al, Comparative Study and Analysis of Classification Algorithms through Machine Learning, International Journal of Computer Engineering and Applications, 9(1),247-252,2018.
- Hany M. Harb1, Malaka A. Moustafa, Selecting optimal subset of features for student performance model, IJCSI , 9(5), 2012, 1694-0814
- Hwang, Young-Sup,Wrapper-based Feature Selection Using Support Vector Machine, Department of Computer Science and Engineering, Sun Moon University, Asan, Sunmoonro, Korea, Life Science Journal,11 (7), 221-70,2014.
- Wang Liping, Feature Selection Algorithm Based On Conditional Dynamic Mutual Information, International Journal Of Smart Sensing and Intelligent Systems,8(1), 2015.
- Qinbao Song, Jingjie Ni and Guangtao Wang, A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data, IEEE Transactions on Knowledge and Data Engineering, 25(1), 2013.
- Z.Zhao, H.Liu, On Similarity Preserving Feature Selection, IEEE Transactions on Knowledge and Data Engineering, 25(3), 2013.
- Sunita Beniwal and Jitender Arora, Classification and Feature Selection Techniques in Data Mining, International Journal of Engineering Research & Technology (IJERT), 1(6), 2012.
- Mital Doshi and Setu K Chaturvedi, Correlation Based Feature Selection (Cfs) Technique To Predict Student Perfromance, International Journal of Computer Networks & Communications (IJCNC),6(3),2014.
- M. Ramaswami and R. Bhaskaran,A Study on Feature Selection Techniques in Educational Data Mining, Journal Of Computing,1(1),December 2009.
- K.Sutha and J. Jebamalar Tamilselv, A Review of Feature Selection Algorithms for Data Mining Techniques, International Journal on Computer Science and Engineering (IJCSE), 7(6), ,2015.
- Gnanambal S and Thangaraj M, A new architectural framework for Rule Based Healthcare System using Semantic Web Technologies, International Journal of Computers in Healthcare, Inderscience, 2(1), 2014, 1-14. ISSN: 1755-3202.