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Data mining case study pdf - Applications of Data Mining in e-Governance A Case Study of Bhoomi Project SpringerLink

An accuracy rate of 95 was achieved by the above prediction process. Data mining is done using new data set and present repository to obtain greater degree of accuracy.

A collection of 16772 cancer cases related to P53 mutations, have been explored to detect and categorize the P53 cancer mutants and their rescue mutants in silico through Data Mining techniques. Differing provisions from the publisher s actual policy or licence agreement may be applicable. Article International Journal of Science and Research IJSR March 2015 Journal of scientific and industrial research Impact Factor 0.


However, enhanced classification outcomes require tuning the randomness and tree growing parameters of the Random Forests algorithm. The proposed techniques take advantage of an appropriate data quantification approach and aim to facilitate the knowledge discovery process of data and contribute to the revelation of the desired information.

Early and precise detection of genetic mutations is a demanding task in the field of bioinformatics and molecular biology, while the accurate identification of rescue mutations presents great therapeutic remedies.

June 2013 Protein and Peptide Letters Impact Factor 1. The results indicate that using fuzzy techniques is a promising approach towards mitigating the negative effects of the aforesaid uncertainty in SoftGIS datasets. Lucia, Brisbane, Australia JIAN PEI School of Computing Science, Simon Fraser University, Canada OSMAR ZA ANE Department of Computing Science, University of Alberta, Alberta, Canada T6G 2E8, Canada We report on the panel discussion held at the ICDM 10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made significant real-world impact. SoftGIS data mining and analysis A case study of urban impression in Helsinki SoftGIS data mining and analysis A case study of urban impression in Helsinki JavaScript is disabled for your browser.


This prediction mechanism was implemented to predict the turnover of a company on an everyday basis and hence could help navigate through dubious stock market trades. This resulted in discovery of interesting associations between the SoftGIS data and the neighboring building types. However, similar to any other large spatial dataset, the SoftGIS data requires a set of spatial analysis and data mining techniques in order to yield the desired information and to be considered as a reliable source of knowledge.


Several experiments inspired by observation and literature illustrate the potentials of computer-based model in classifying a number of bank history records. Furthermore, this thesis widened its domain of knowledge discovery to a less explicit realm of information through employing spatial data mining. The authorized dataset for predicting the turnover was taken from www. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation. Data-driven Discovery of Quantitative Rules in Relational Databases.

Full-text Conference Paper Dec 2013 International Journal of Science and Research IJSR ABSTRACT Several oncogenic malignancies show evidence of carrying mutations in the TP53 gene causing defects in the genome maintenance mechanisms that tend to instigate cancer. This study tries to help the investors in the stock market to decide the better timing for buying or selling stocks based on the knowledge extracted from the historical prices of such stocks. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.

The tasks covered by 10 case studies range from the detection of anomalies such as cancer, fraud, and system failures to the optimization of organizational operations, and include the automated extraction of information from unstructured sources. Full-text Conference Paper Dec 2013 International Journal of Science and Research IJSR ABSTRACT Several oncogenic malignancies show evidence of carrying mutations in the TP53 gene causing defects in the genome maintenance mechanisms that tend to instigate cancer., Seattle, WA 98126, USA XINDONG WU Department of Computer Science, University of Vermont, Burlington, VT 05405, USA PAUL BEINAT NeuronWorks International, Hurtsville, NSW 2220, Australia LONGBING CAO University of Technology, Sydney, Australia RONG DUAN AT T Labs, Research, Florham Park, NJ, USA CHRISTOS FALOUTSOS Department of Computing Science, Carnegie Mellon University, 5000 Forber Avenue, Pittsburgh, PA 15213, USA RAYID GHANI Accenture Technology Labs, 161 N.


Clark St, Chicago, IL 60601, USA BRENDAN KITTS Lucid Commerce, Seattle, WA 98104, USA BART GOETHALS Department of Mathematics and Computer Science, University of Antwerp, Belgium GEOFF MCLACHLAN Department of Mathematics, University of Queensland, St.

The motive here is to apply data mining techniques to brain data that are obtained from EEG to differentiate between brain dead and coma patients. 2012 Applications of Data Mining in e-Governance A Case Study of Bhoomi Project. The authorized dataset for predicting the turnover was taken from www. TOP-10 DATA MINING CASE STUDIES International Journal of Information Technology Decision Making, Vol 11, No 02 World Scientific 10. This empirical research aims to evaluate the performance of different Machine Learning algorithms for credit risk prediction with more focus on Random Forest Trees. The two major problems encountered in this process are the high dimensionality of data with comparatively few instances and the need to categorize records under multiple.

Moreover, the importance of stock market attributes was established as well. With these extracted features, the Total Turnover of the company was predicted using various classification algorithms like Random Forest, Decision Tree, SVM and Multinomial Regression.

Identification of new rescue mutants is expected to offer remarkable advancement in the field of cancer therapy targeting drugs to specifically restore normal P53 activity.


The objective of this paper is to investigate various classification algorithms to predict the turnover of different companies based on the Stock price. R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics.


Article International Journal of Science and Research IJSR March 2015 Journal of scientific and industrial research Impact Factor 0. Department of Computer Science Applications Bangalore University Bangalore India About this paper Cite this paper as Hanumanthappa M.

Some features of this site may not work without it. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.


Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work Export file RIS for EndNote, Reference Manager, ProCite BibTeX Text RefWorks Direct Export Content Citations Only Citations and Abstracts Entitled to full text, Pages 1-4 Not entitled to full text, Pages 105-122 Not entitled to full text, Pages 123-136 Not entitled to full text, Pages 137-150 Not entitled to full text, Pages 151-179 Not entitled to full text, Pages 181-211 Not entitled to full text, Pages 213-219 Not entitled to full text, Pages 221-224 Not entitled to full text, Pages 225-228 Not entitled to full text, Pages 229-230 Not entitled to full text, Pages 233-234 Cookies are used by this site.

Turnover Prediction of Shares Using Data Mining Techniques A Case Study 13 Sri Nadar College of Engineering Predicting the turnover of a company in the ever fluctuating Stock market has always proved to be a precarious situation and most certainly a difficult task in hand. ABSTRACT One of the major challenges in medical history has been between a brain dead person and a person in coma.

SoftGIS data mining and analysis A case study of urban impression in Helsinki Title SoftGIS data mining and analysis A case study of urban impression in Helsinki Author s Date 2014-05-19 Language en Pages 48 2 Major Subject Geoinformatics Supervising professor s Virrantaus, Kirsi Thesis advisor s Nikander, Jussi Ahonen-Rainio, Paula Keywords Location In recent years there has been considerable breakthrough in acquisition of qualitative georeferenced data. In this research investigation, our aim is to identify potential P53 cancer-causing mutants and predict possible rescue mutations at secondary-site DNA binding domains. Finally, we find that successful applications are more commonly associated with continual improvement rather than by single aha moments of knowledge nugget discovery. The decision taken will be based on decision tree classifier which is one of the data mining techniques.


An accuracy rate of 95 was achieved by the above prediction process. The results indicate that using fuzzy techniques is a promising approach towards mitigating the negative effects of the aforesaid uncertainty in SoftGIS datasets. Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. The Naive Bayes probability values of the amino-acid substitutions at the respective site-wise recombination were utilized to formulate the proposed Genetic Mutant Marker. on Knowledge Discovery and Data Mining, Portland, OR, pp. The authorized dataset for predicting the turnover was taken from www. The proposed techniques take advantage of an appropriate data quantification approach and aim to facilitate the knowledge discovery process of data and contribute to the revelation of the desired information. Data mining is done using new data set and present repository to obtain greater degree of accuracy.


Differing provisions from the publisher s actual policy or licence agreement may be applicable. Such environment and behavior create unreliable base for strategic planning and risk management. These considerations could not be accomplished without studying the data and exploring its specific However, similar to any other large spatial dataset, the SoftGIS data requires a set of spatial analysis and data mining techniques in order to yield the desired information and to be considered as a reliable source of knowledge. This thesis propounded a four stage knowledge discovery process in which several exploratory, visual and analytical spatial techniques were proposed.

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