Description
This week, answer the following questions in an essay format:
- How does data and classifying data impact data mining?
- What is association in data mining?
- Select a specific association rule (from the text) and thoroughly explain the key concepts.
- Discuss cluster analysis concepts.
- Explain what an anomaly is and how to avoid it.
- Discuss methods to avoid false discoveries.
This assignment should take into consideration all the course concepts in the book. Be very thorough in your response. The paper should be at least three pages in length and contain at least two-peer reviewed sources.
Explanation & Answer
View attached explanation and answer. Let me know if you have any questions.
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Introduction to Data Mining (Week15)
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Introduction to Data Mining (Week15)
Impact on Data Mining
Data and classifying data impact data mining by allocating items in a collection to target
categories or classes. Classification aims to correctly forecast the objective class from every case
in the data. For instance, a classification model can be used to help understand debtors' risk
levels. Classification is a data mining technique and helps to retrieve essential and valuable
information about data and metadata. Classification in data mining helps to classify data in
various classes. The classes include classification of data mining frameworks as per the type of
data sources mined, as per database involved and the type of knowledge discovered. Examples of
data sources mined include multimedia, spatial data, text data, time-series data, and World Wide
Web. There is also a classification of data mining frameworks based on data mining techniques
used, For example, machine learning, genetic algorithms, visualization, and statistics (Tan et al.,
2019).
Association in Data Mining
Association in data mining uses machine learning models to analyze data for patterns in a
database. Association rules are formed by searching data for patterns, using particular support,
and focusing on finding crucial relationships. Association analysis is the act of locating
interesting relationships in big data sets. These relationships come in two ways: frequent itemsets
or association rules. Frequent itemsets are a collection of items...