Question

Topic: The Need for Data Mining Overview: Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious, and time consuming data mining practices to quick, easy, and automated collection for data analysis. The more complex the data sets, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers, and insurers are using data mining to discover relationships among everything from price optimization, promotions, and demographics to how the economy, risk, competition, and social media are affecting their business models, revenues, operations, and customer relationships. SELECT BANKING/FINANCE Include the following critical elements in your essay: 1)Use Case: Present an industry use case discuss the need for data mining in this particular industry. Describe how data mining can help organizations within the selected industry to retrieve valuable information from the huge amount of data they collect. Explain how they make the data usable for analytical purposes, for business use, or for strategic planning purposes as a result of data mining processes. II. Data Mining Challenges: Describe one challenge associated with data mining practices and processes within this industry. How can organizations overcome this challenge to gain a competitive advantage over their competitors? III. Data Mining Techniques: Describe one data mining technique that would be useful for collecting the data required to support the industry and foster effective data analysis processes for decision-making (clustering, classification, pattern matching, association, regression, visualization, or meta rule-guided mining, for example). How will this technique help organizations in this industry overcome the challenge you mentioned?

Answer 1

(1)(i)Ans Let's take the use case of finance industry.
Due to economic globalization and information
technology evolution huge amount of financial data
is produced. Efficient and effective utilization of
this huge data is needed which can be done with the
help of data mining. Data mining is a field which bring
machine learning,statistics,pattern recognition,visualization
databases together so as to extract information from
such a huge amount of data.

(1)(ii)Ans Various techniques of data mining can help
organizations within the finance industry to retrieve
valuable information from the huge amount of data
they collect. Some of them are listed below:

1. Classification: It is used to classify and simplify
data into groups.It is most common algorithms which
is used in data mining i.e neural network and tree decisions

2. Clustering: It is similar to classification. The difference
lie in the fact that it club object with similar characterstics
in a class by using automation.

3.Sequential Pattern: It is a technique which helps in
discovering and extracting the redundant behaviors in
the collected finance data stored in a database.

4.Outer Detection: Process of detecting those data items
which are not comply with the dataset general model. It
is also called as outlier mining or outlier analysis.
This technique plays a major role in fraud detection like
credit card fraud detection in finance industry. Also it
able to detect fault, intrusion e.t.c.

5.Regression: A statistical tool used in data analysis.
It is used to identify the relationship among various kind
of data treated as variables.

(1)(iii)Ans In financial sector data mining can be used
to find the secret patterns,investment,to do
risk management, decision making having critical
importance, future trends and characterstics
in financial markets.In this way data mining make the
data usable for analytics purposes, for business use
and for strategic planning purposes.

Note: Please ask the other questions separately.