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Different Techniques In The Data Mining Process In Data Science
Data Science is an umbrella term that includes a lot of process, tools, techniques & algorithms to accurately interpret with large sets of Big Data. Data Mining is one of the crucial steps in the data analysis process in Data Science that helps enterprises in mining accurate insights from their large reserves of Big Data. When it comes to identifying particular trends, patterns & to find relations between different data sets then, the best technique that is used by Data Scientists is Data Mining.
Data Mining is a crucial skill in the inventory of Data Scientist that requires intensive skills in Machine Learning, Statistics, AI and database technology to be able to handle its operations in a precise manner. Data Mining is used for varied applications like, but not limited to fraud detection, scientific discovery, risk analysis, etc. Work towards mastering the skills in Data Mining & other prominent techniques that are associated with Data Science with the help of our advanced Data Science Training In Hyderabad program.
Different Techniques In The Data Mining process in Data Science:
- Outer Detection
This technique is also referred to as Outlier Analysis or Outlier Mining technique. This form of Data Mining technique can be used on the data sets whose behavior doesn’t match with the expected behavior or pattern. Intrusion, detection, fraud or fault detection, etc. are the areas where this form of Data Mining technique is extensive used.
- Sequential Patterns
If you are intended to discover similar patterns or trends in the data then you should be using Sequential Pattern technique. The extensive use of this technique can be seen in the sectors that deal with large sets transactional data like Insurance, Banking, & across other sectors.
This form of Data Analysis technique is used in association with other techniques like trends, sequential patterns, clustering, classification, etc. Using this technique, we can accurately predict the chances of the occurrence of future events by analyzing present & historical data.
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