What is imputation in data mining?
In statistics, imputation is the process of replacing missing data with substituted values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results.
Why do we need pre processing data?
Data preprocessing is crucial in any data mining process as they directly impact success rate of the project. Data is said to be unclean if it is missing attribute, attribute values, contain noise or outliers and duplicate or wrong data. Presence of any of these will degrade quality of the results.
Why data preprocessing is important in data mining?
How is data preprocessing used in data mining?
Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format. Steps Involved in Data Preprocessing: 1. Data Cleaning: The data can have many irrelevant and missing parts. To handle this part, data cleaning is done.
How is data reduction used in data mining?
Data Reduction: Since data mining is a technique that is used to handle huge amount of data. While working with huge volume of data, analysis became harder in such cases. In order to get rid of this, we uses data reduction technique. It aims to increase the storage efficiency and reduce data storage and analysis costs.
How is data transformation used in data mining?
Data Transformation: This step is taken in order to transform the data in appropriate forms suitable for mining process. This involves following ways: In this strategy, new attributes are constructed from the given set of attributes to help the mining process.
What does noisy data mean in data mining?
Noisy data is a meaningless data that can’t be interpreted by machines.It can be generated due to faulty data collection, data entry errors etc. It can be handled in following ways : This method works on sorted data in order to smooth it.