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Q1: Describe the differences between structured and unstructured data. Explain structured data in big data environment and give one (1) example of machine generated structured data.
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.
Q1.
Describe the differences between structured and unstructured data.
Structured data are organised into a formatted pattern while unstructured data do not follow pattern. Here pattern is more relevant to machines rather than humans.
Structured data can be stored in databases with well-defined schemas while the unstructured data cannot be stored in a tabular form with defined attributes.
Due to defined attributes, structured data can be indexed for which a data is easily searchable following some standard searching algorithms. In unstructured data, the search operation is tedious since the data do not follow specific indexing and mostly followed lexicographic model as in dictionaries.
Transaction management and other concurrency control operations can be easily implemented in structured data while no such consistency and reliability measures can be taken in an unstructured data.
Structured data suffers from scalability problem while unstructured data are easily scalable.
Explain structured data in big data environment
Data that are created in large volumes are called Big data. These data are continuously generated in the form of structured data from broadly two sources.
Machine generated data are generally produced by the specific machines involved within specific domains. These machines are generally fully automated except the operational procedures that may require partial human intervention.
Give one (1) example of machine generated structured data.
The level of potassium under the root of a plant is being continuously monitored and measured by a sensor where the relevant data (of course structured) is being captured and sent to a computing system for analysis.
Q2:
What does data pre-processing mean in Data Mining
Data pre-processing is a method of converting a raw data into such a format so that an analytical system can read the prepared data without inconsistency and biasness and perform analysis with higher accuracy.
Why is it important?
Raw data suffers from:
Due to the above problems, the analysis will be biased, erroneous and far from accuracy. In order to perform more accurate analysis, the system must be provided with clean, processed, structured and error-free data.
Explain the five (5) steps in data pre-processing?
Q3:
What is an outlier?
Among a set of observations, an observation that lies at a significantly different distance from the other values in a random sample is called an outlier.
Why outlier detection is important in some of the data mining applications?
Outlier detections are very important because they affect the mean and median that eventually generates a deviation along the error line in any data set. This deviation can potentially form a bias towards an analysis performed on the dataset. In some applications, this deviation or distorted cluster formation (in case of clustering technique) indicates that there are outliers present in the dataset that appears ‘unnatural’ to the general trend of the dataset.
Give examples of such applications.
Pleaaase help me :(( I need new and unique answers, please. (Use your own words, don't...
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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. Please, Please, Please and Please… 1. I need new and unique answers, please. (Use your own words, don't copy and paste, even when you answer like theses answers before.) 2. Please Use your keyboard to answer...
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