Question

Q2: What does data pre-processing mean in Data Mining and why is it important? Explain the five (5) steps in data pre-processing?

Q3: What is an outlier? Why outlier detection is important in some of the data mining applications? Give examples of such applications.

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Answer 1

Data preprocessing in Data mining :

Data preprocessing is a type of extracting information from huge set of data, that involves transforming raw data into an understandable format.

importance of data preprocessing :

1.Data Cleaning : missing data,ignoring tuple,clustering

2.Data transformation : normalization,attribute selection,discretization,

3.Data reduction : data cube aggregation,dimentionally reduction

five Steps in Data preproccessing :

• Data Cleaning: Data is cleaned through processes such as filling in missing values, smoothing the noisy data, or resolving the inconsistencies in the data.
• Data Integration: Data with different representations are put together and conflicts within the data are resolved.
• Data Transformation: Data is normalized, aggregated and generalized.
• Data Reduction: This step aims to present a reduced representation of the data in a data warehouse.
• Data Discretization: Involves the reduction of a number of values of a continuous attribute by dividing the range of attribute intervals. Outlier : An Outlier is a rare chance of occurrence within a given data set. In Data Science, an Outlier is an observation point that is distant from other observations. Importance of outlier detection in some of Data mining applications :
• Outlier detection aims to find patterns in data that do not conform to expected behavior.
• Their importance in data is due to the fact that they can translate into actionable information in a wide variety of applications.
• Outliers in credit card transaction data could indicate credit card or identity theft or abnormal readings from a space craft sensor could signify a fault in some component of the space craft. Examples : The notion of outlier.
• Nature of the data.
•  Challenges associated with detecting outliers.
•  Existing outlier detection technique