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Data Science Techniques for Efficient Data Mining

As more and more organizations rely on analytics to help gain insights into problems, they require efficient means to gain insights into the diverse types of data available. Finding and extracting intelligence from the available data has thus become essential. Data science techniques can help structure this data from different sources. Techniques for handling data from text, sequences, time series, space and graphs etc. Help increase the power and accuracy of analytic solutions.

Mining different kinds of knowledge in a database

Data mining should cover a large spectrum of data analysis and knowledge discovery tasks. These include: data characterization, separation, association and correlation analysis, classification, prediction, clustering, outlier analysis, and evolution analysis (including also trend and similarity analysis). All these tasks use the same database in different ways using different data mining techniques.

Interactive mining of knowledge at multiple layers of generality

For databases containing tremendous amount of data, appropriate sampling techniques with interactive mining facilitates more refined requests and outcomes. Users can then interact with data mining system to view and discover patterns at various levels of granularity

 Data mining query languages and ad hoc data mining

Relational query languages such as SQL allow users to use ad hoc queries or data retrieval. Such a language should be blended with a database or data warehouse query language and optimised for efficient and flexible data mining.

Presentation and visualization of data mining

The discovered data must have a visual representation so that the knowledge can be easily understood and directly applied by humans.

Handling noisy data

Sometimes, data stored in a database may include noise and incomplete data objects. When mining such irregular data, the accuracy of the discovered patterns may be poor. Data cleaning methods are hence required to clear such noise. The first step in the process of data cleaning is discrepancy detection.

Efficient data mining can be of great help in data analytics and businesses can benefit from convenience of data availability to make strategic business decisions.


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