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Gnanapriya, S.
- Data Mining Concepts and Techniques
Abstract Views :549 |
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Authors
Affiliations
1 Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, IN
2 Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore
1 Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, IN
2 Department of Computer Science, Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore
Source
Data Mining and Knowledge Engineering, Vol 2, No 9 (2010), Pagination: 256-263Abstract
Understand the need for analyses of large, complex, information-rich data sets.
- Identify the goals and primary tasks of the data-mining process.
- Describe the ischolar_mains of data-mining technology.
- Recognize the iterative character of a data-mining process and specify its basic steps.
- Explain the influence of data quality on a data-mining process.
- Establish the relation between data warehousing and data mining.
Data mining is an iterative process within which progress is defined by discovery, through either automatic or manual methods. Data mining is most useful in an exploratory analysis scenario in which there are no predetermined notions about what will constitute an "interesting" outcome. Data mining is the search for new, valuable, and nontrivial information in large volumes of data. It is a cooperative effort of humans and computers. Best results are achieved by balancing the knowledge of human experts in describing problems and goals with the search capabilities of computers.
In practice, the two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories:
- Predictive data mining, which produces the model of the system described by the given data set, or
- Descriptive data mining, which produces new, nontrivial information based on the available data set.
Keywords
Data Mining, Prediction and Decision-Support.
- Sentiment Analysis Based on Fine Grained Feature Representation Of Domain Sentiment Dictionary
Abstract Views :116 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Applications, Kovai Kalaimagal College of Arts and Science, India., IN
1 Department of Computer Applications, Kovai Kalaimagal College of Arts and Science, India., IN
Source
ICTACT Journal on Soft Computing, Vol 13, No 3 (2023), Pagination: 2931-2939Abstract
The era of information technology has grown tremendously over the decade. In the existing opinion mining technology, the response time and the accuracy are not up to the expectation. The sentiment analysis is not accurate in the traditional systems. The method failed to divide the opinion holder, tendency and the opinion object from the opinion given by the holder/user and it results to the failure in obtaining the overall report of the positive and negative feedbacks of the object. The proposed fine grained opinion mining can perform better in analyzing the holder, tendency, and expression from the statement. This accuracy of the proposed system is consistent in the case of large datasets such as reading the reviews of the customers and filters the positive and negative opinion in an accurate manner. The proposed system uses the external sentiment directory for comparing the opinion given by the user and predefined emotional data stored in the directory.Keywords
Opinion Mining, Sentiment Analysis, Emotional Mining, Conditional Random Field, Machine Learning.References
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