Data Mining Applications and Use Cases

1. Objective

We studied in our last session, Data Mining Process. Here, we will explore the Data Mining Applications. Also, we will cover Data Mining Use Cases of each and every field.

So, let’s start Data Mining Applications.

Data Mining Applications and Use Cases

Data Mining Applications and Use Cases

2. Data Mining Applications & Use Cases

Following are the applications of data mining in various sectors:

a. Data Mining in Finance

  • We have to Increase customer loyalty by collecting and analyzing customer behavior data. Also, one needs to help banks that predict customer behavior and launch relevant services and products.
  • Helps in Discovering hidden correlations between various financial indicators that need to detect suspicious activities with a high potential risk.
  • Generally, it identifies fraudulent or non-fraudulent actions. As it done by collecting historical data. And then turning it into valid and useful information.

Do you know What are Data Mining Techniques?

b. Data Mining in Healthcare

  • Basically, it provides government, regulatory and competitor information that can fuel competitive advantage. Although, it supports the R&D process. And then go-to-market strategy with rapid access to information at every phase.
  • Generally, it discovers the relationships between diseases and the effectiveness of treatments. That is to identify new drugs or to ensure that patients receive appropriate, timely care.
  • Also, it supports healthcare insurers in detecting fraud and abuse.

c. Data Mining for Intelligence

  • Generally, it reveals hidden data related to money laundering, narcotics trafficking, etc.
  • Also, helps in Improving intrusion detection with a high focus on anomaly detection. And identify suspicious activity from a day one.
  • Basically, convert text-based crime reports into word processing files. That can be used to support the crime-matching process.

d. Data Mining in Telecommunication

  • In this, data mining gains a competitive advantage and reduce customer churn by understanding demographic characteristics and predicting customer behavior.
  • Increases customer loyalty and improve profitability by providing customized services.
  • As it supports customer strategy by developing appropriate marketing campaigns and pricing strategies.

e. Data Mining for Energy

  • As data mining capture weak signals of potentially threatening events. Also, identify previously unidentified patterns, connections.
  • Structure identification of important information, and distill it to boost technical problem-solving. Also, empower more informed decision-making and enable immediate notification of prospective technical breakthroughs.
  • Improve core processes in upstream, midstream and downstream. As with analysis and intelligence capabilities using a variety of sources.

Let’s discuss Knowledge Discovery Database(Kdd Process) in Data Mining

f. Data Mining in Marketing and Sales

  • Basically, it enables businesses to understand the hidden patterns inside historical purchasing transaction data. Thus helping in planning and launching new marketing campaigns.
  • Generally, the following illustrates several data mining applications in sale and marketing.
  • We use it for market basket analysis. That is to provide information on what product combinations have to purchased together. This information helps businesses promote their most profitable products and maximize the profit. In addition, it encourages customers to purchase related products.
  • Retail companies use data mining to identify customer’s behavior buying patterns.

g. Data Mining in E-commerce

Many E-commerce companies are using data mining business Intelligence to offer cross-sells through their websites. One of the most famous of these is, of course, Amazon. They use sophisticated mining techniques to drive their ‘People who viewed that product. Also liked this’ functionality.

h. Data Mining in Biological Data Analysis

In recent times, we have seen a tremendous growth in the field of biologies. Such as genomics, proteomics, functional Genomics and biomedical research. Also, Biological data mining is a very important part of Bioinformatics.
Following are the aspects in which data mining contributes for biological data analysis −
  • Semantic integration of heterogeneous, distributed genomic and proteomic databases.
  • Alignment, indexing, similarity search and comparative analysis multiple nucleotide sequences.
  • Discovery of structural patterns and analysis of genetic networks and protein pathways.
  • Association and path analysis.
  • Visualization tools in genetic data analysis.

i. Data Mining for Crime Agencies

Beyond corporate applications of Data Mining, crime prevention agencies use analytics. And Data Mining to spot trends across myriads of data. That should help with everything from where to deploy police manpower. And Particularly who to search at a border crossing. And even which intelligence to take seriously in counter-terrorism activities.

j. Data Mining in Retail

Generally, retailers segment customers into ‘Recency, Frequency, Monetary’ groups. Also, in target marketing and promotions to those different groups. A customer who spends little but often and last did so recently will be handled by a customer. Particularly who spent big but only once, and also some time ago.
  • Large retailers like Walmart utilize information on store footfall, advertising campaign even weather forecast to predict sales and stock up accordingly.
  • Credit card companies mine transaction records for fraudulent use of their cards. That was based on purchase patterns of consumers. As they can deny access if your purchase patterns change drastically!

Read This  – Classification Algorithms of Data Mining

  • In Genomics, Research gathers Speed using Computational Methods
  • Generally, the Human Genome Project mounts up piles of data. Although, getting the data to work for humankind need to develop a new drug and weed out diseases. That will require pattern recognition in the data which is handled in bioinformatics.
  • As scientists use microarray data to look at the gene expressions. And also sophisticated data analysis techniques. That is employed to account for the background noise and normalization of data.

k. Data Mining for Information Retrieval

Terabytes of data are being accumulated on the internet. That includes Facebook and Twitter and other social networking sites. We can say as this vast repository may be mined and controlled to some extent.

l. Data Mining in Communication Systems

Speech recognition is one important area of a communication system. As it’s an important pattern recognition methods. That were developed and have been transferred to other Data Mining application areas.
Image analysis is another important area of data mining applications. Also, facial recognition techniques are a part of security arrangements.

m. Data Mining in Education

There is a newly emerging field, called Educational Data Mining. As it concerns with developing methods. That discover knowledge from data originating from educational Environments. The goals of EDM are identified as predicting students’ future learning behavior, studying. We use data mining by an institution to take accurate decisions. And also to predict the results of the student. With the results, the institution can focus on what to teach and how to teach. Learning pattern of the students can be captured. And used to develop techniques to teach them.

n. Data Mining in Manufacturing Engineering

We use data mining tools to discover patterns in the complex manufacturing process. Although, data mining can be used in system-level designing. That need to extract the relationships between product architecture, portfolio. Also, we use it to predict the product development span time, cost.

o. Other Scientific Applications

The Data Mining applications discussed above tend to handle small and homogeneous data sets. As for which the statistical techniques are appropriate. A huge amount of data have been collected from scientific domains. A large number of data sets is being generated. Because of the fast numerical simulations in various fields. Such as climate and ecosystem modeling, chemical engineering, fluid dynamics, etc.
Following are the applications of data mining in the field of Scientific Applications –
  • Data Warehouses and data preprocessing.
  • Graph-based mining.
  • Visualization and domain-specific knowledge.

So, this was all about Data Mining Applications & Use Cases. Hope you like our explanation.

3. Conclusion

As a result, we have studied Data Mining Applications.  Also, we have covered each and every application belonging to each domain. Hence, it is helpful to understand the uses of data mining. STill, have a confusion? Share your feedback with us!
Related Topic –  DMQL

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