DDM VS TRADITIONAL DATA MINING:
Traditional Data mining needs to aggregate distributed data into single cubical collection and process it to derive knowledge from it. Even though it collects data in a distributed network the main soul of the framework is a centralized one, which is inappropriate for distributed mining application because parameters like long response time, lack of proper use of distributed resources. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit.
The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:
• Selection
• Pre-processing
• Transformation
• Data Mining
• Interpretation/Evaluation.
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For example if we consider this strategy for a mobile ad hoc network where different sensor nodes are monitoring certain time event, then central collection of data from different nodes is a tedious process and may lead to heavy traffic in the channel, So this not advisable.
While coming to DDM a distributed frame work may reduce load on the communication network and reduces data security issues. So, for the same above example this strategy prevents channel trafficking and even save the battery usage over the system of PDAs, as this architecture is intended for...
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...n comparison with the traditional methodologies of data management in a network. We even discuss the functioning algorithm of this strategy with its privacy issues and their addressing techniques. As data management is being extended into every field, there is a need for many other sever techniques and strategies for effective performance. Current mining are designed with effective standards but still there are major open issues which need to be addressed by the DDM especially in the cases of data pre-processing. Exchange of mining models across participating site would be an interesting step in future enhancements.
References:
• Intel study on distributed data mining on big data--- http://www.oracle.com/events/emea/en/oracleday2012/intel-vision-paper-1878780.pdf
• Wikipedia
• A paper on distributed data mining and Agents by Josenildo C da Silva, Chris Giannella.
The SWOT analysis involves four steps. They are strength, weakness, opportunity, and threats. This will assist you to ident...
Data aggregation is a type of data mining and/or processing of data that is not meaningful in its form, where it can be transformed into meaningful information that can be related to a specific person or thing. Another definition of data aggregation is “data aggregation is a type of data and information mining process where data is searched, gathered and presented in a report-based, summarized format to achieve specific business objectives or processes and/or conduct human analysis.” Data aggregation is a double sword edge that can be devoted for good or bad proposes. After attack of September 11,2001 a private company that owned by Hank Asher, has developed a complex algorithm that search and s...
35 Meng Xiaofeng and Ci Xiang, 2013 : Big Data Management: Concepts,Techniques and Challenges pp 4-6
Quantitative methods in the social sciences are an effective tool for understanding patterns and variation in social data. They are the systematic, numeric collection and objective analysis of data that can be generalized to a larger population and seek to find cause in variance (Matthews and Ross 2010, p.141; Henn et al. 2009, p.134). These methods are often debated, but quantitative measurement is important to the social sciences because of the numeric evidence that can be used to drive more in depth qualitative research and to focus regional policy, to name a few (Johnston et al. 2014). Basic quantitative methods, such as descriptive and inferential statistics, are used regularly to identify and explain large social trends that can then
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Mining frequent disease from medical data using association rule mining technique Abstract Health care industry today generates large amounts of complex data for patients, hospitals facilities, diseases, disease diagnostic methods, electronic patients records, etc .The data mining techniques are very useful to make medicinal decisions, specially to analyze the information about frequently occurring disease from large dataset obtained from hospital so that in future healthcare administrator will able to improve the quality of service. Until now some works done on this where they had collected medical data from particular area and improve the accuracy of classification, increase in the prediction of various diseases. But here in the proposed technique work will be done on mining the frequent disease of patients in different geographical area at given time period.
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[3] B. Thuraisingham. Data mining, national security, privacy and civil liberties. In ACM SIGKDD Explorations, Volume 4 Issue 2, page 1-5. New York, 2002.
A data warehouse comprised of disparate data sources enables the “single version of truth” through shared data repositories and standards and also provides access to the data that will expand frequency and depth of data analysis. Due to these reasons, data warehouse is the foundation for business intelligence.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
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