data mining lab viva question and answers
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Data mining is the computational process of discovering patterns in large datasets involving methods at the intersection of artificial intelligence, automated learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The general objective of the data mining process is to extract information from a set of data and transform it into a structure understandable for later use. Apart from the raw analysis step, it involves aspects of data management and databases, data preprocessing, model considerations and inference, interest metrics, complexity considerations, postprocessing of discovered structures, online visualization and updating. Data mining is the step of analyzing the process of "knowledge discovery in databases", or KDD.

Answers to short questions

Q What is data mining?
A data mining is the discovery of knowledge in databases. It is the extraction of interesting information (not trivial, implicit, previously unknown and potentially useful) or patterns of data in large databases.

Q What is the difference between OLAP and data mining?
An OLAP (Online Analytical Processing) gives you a very good view of what is happening but can not predict what will happen in the future or why it is happening, since data mining is a group of techniques that find relationships Which have not been previously discovered.

Q What types of tasks are performed during data mining?
A data mining involves two types of tasks
• Prediction tasks: use some variables to predict unknown or future values of other variables
• Tasks Description: Find patterns interpretable by the human being that describe the data.

Q What are some of the tasks of data mining?
A The following activities are performed during data mining
• Classification [Predictive]
• Clustering [Descriptive]
Discovery of Association Rules [Descriptive]
• Sequential pattern discovery [Descriptive]
• Regression [Predictive]
• Deviation Detection [Predictive]
Q What does data preprocessing mean in data mining?
A Before extracting the data, it is necessary to preprocess it. It consists of following three stages
• Data cleaning: real-world data is dirty, so you need to clean it
• Data reduction - Eliminate data not useful for mining
• Data transformation - Syntactic transformation
Q What is data cleaning?
A Causes of dirty data
• Missing values
• Noisy data (Human / machine errors)
• Inconsistent data
Data Cleanup Tasks
• Management of lost values
• Identify outliers and smooth out noisy data
• Correct inconsistent data

Q Explain Data reduction? A It consists of following three tasks -
• Dimensional reduction: selection of attribute subsets
• Number Reduction - Selection of subsets of Tuples
• Discretization - Reduce the cardinality of the active domain
Q What is Data Transformation? A Consists of the following tasks
1. Generalization - escalating the conceptual hierarchy
2. Attribute / feature construction - New attributes are built and added to the tuple
3. Normalization - scaled to fall within a specified small range
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