Data mining is a method of extracting data from multiple sources and organizing it to derive valuable insights. Read on to discover the wide-ranging data mining applications that are changing the industry as we know it!
Modern-day companies cannot live in a data lacuna. They have to evolve and keep up with technological evolution and upcoming digital trends to stay ahead of the competition. So, businesses today are prioritizing staying abreast of all the new developments in the field of data science and analytics. Data mining is one such process. Check out the common examples of data mining.
It involves an examination of pre-existing datasets to gain new and useful information. The complex data mining algorithms allow companies to make sense of raw data by segmenting large datasets, identifying patterns, and predicting outcomes.
Let us look at some of the major applications of data mining.
Data Mining Applications
1. Financial Analysis
The banking and finance industry relies on high-quality, reliable data. In loan markets, financial and user data can be used for a variety of purposes, like predicting loan payments and determining credit ratings. And data mining methods make such tasks more manageable.
Classification techniques facilitate separation of crucial factors that influence customers’ banking decisions from the irrelevant ones. Further, multidimensional clustering techniques allow identification of customers with similar loan payment behaviours. Data analysis and mining can also help detect money laundering and other financial crimes. Read more about data science applications in finance industry
2. Telecommunication Industry
Expanding and growing at a fast pace, especially with the advent of the internet. Data mining can enable key industry players to improve their service quality to stay ahead in the game.
Pattern analysis of spatiotemporal databases can play a huge role in mobile telecommunication, mobile computing, and also web and information services. And techniques like outlier analysis can detect fraudulent users. Also, OLAP and visualization tools can help compare information, such as user group behaviour, profit, data traffic, system overloads, etc.
3. Intrusion Detection
Global connectivity in today’s technology-driven economy has presented security challenges for network administration. Network resources can face threats and actions that intrude on their confidentiality or integrity. Therefore, detection of intrusion has emerged as a crucial data mining practice.
It encompasses association and correlation analysis, aggregation techniques, visualization, and query tools, which can effectively detect any anomalies or deviations from normal behaviour.
4. Retail Industry
The organized retail sector holds sizable quantities of data points covering sales, purchasing history, delivery of goods, consumption, and customer service. The databases have become even larger with the arrival of e-commerce marketplaces.
In modern-day retail, data warehouses are being designed and constructed to get the full benefits of data mining. Multidimensional data analysis helps deal with data related to different types of customers, products, regions, and time zones. Online retailers can also recommend products to drive more sales revenue and analyze the effectiveness of their promotional campaigns. So, from noticing buying patterns to improving customer service and satisfaction, data mining opens many doors in this sector.
5. Higher Education
As the demand for higher education goes up worldwide, institutions are looking for innovative solutions to cater to the rising needs. Institutions can use data mining to predict which students would enrol in a particular program, who would require additional assistance to graduate, refining enrollment management overall.
Moreover, the prognosis of students’ career paths and presentation of data would become more comfortable with effective analytics. In this manner, data mining techniques can help uncover the hidden patterns in massive databases in the field of higher education.
6. Energy Industry
Big Data is available even in the energy sector nowadays, which points to the need for appropriate data mining techniques. Decision tree models and support vector machine learning are among the most popular approaches in the industry, providing feasible solutions for decision-making and management. Additionally, data mining can also achieve productive gains by predicting power outputs and the clearing price of electricity.
7. Spatial Data Mining
Geographic Information Systems (GIS) and several other navigation applications make use of data mining to secure vital information and understand its implications. This new trend includes extraction of geographical, environment, and astronomical data, including images from outer space. Typically, spatial data mining can reveal aspects like topology and distance.
8. Biological Data Analysis
Biological data mining practices are common in genomics, proteomics, and biomedical research. From characterizing patients’ behaviour and predicting office visits to identifying medical therapies for their illnesses, data science techniques provide multiple advantages.
Some of the data mining applications in the Bioinformatics field are:
- Semantic integration of heterogeneous and distributed databases
- Association and path analysis
- Use of visualization tools
- Structural pattern discovery
- Analysis of genetic networks and protein pathways
9. Other Scientific Applications
Fast numerical simulations in scientific fields like chemical engineering, fluid dynamics, climate, and ecosystem modeling generate vast datasets. Data mining brings capabilities like data warehouses, data preprocessing, visualization, graph-based mining, etc.
10. Manufacturing Engineering
System-level designing makes use of data mining to extract relationships between portfolios and product architectures. Moreover, the methods also come in handy for predicting product costs and span time for development.
11. Criminal Investigation
Data mining activities are also used in Criminology, which is a study of crime characteristics. First, text-based crime reports need to be converted into word processing files. Then, the identification and crime-machining process would take place by discovering patterns in massive stores of data.
Sophisticated mathematical algorithms can indicate which intelligence unit should play the headliner in counter-terrorism activities. Data mining can even help with police administration tasks, like determining where to deploy the workforce and denoting the searches at border crossings.
Choosing a data mining system
Data mining lies at the junction of machine learning, statistics, and database systems. As we discussed earlier, it can empower modern-day industries in diverse ways. The selection of a suitable data mining system generally depends on the following factors.
- Type of Data: Before choosing a mining system, we need to check the format of data that its existing infrastructure can handle. The data can be record-based, relational, or in the form of ASCII text, database or warehouse data, etc.
- Type of Sources: Data sources surface as another consideration while selecting a data mining system. Some data mining systems work on relational sources, while others may operate only on ASCII text files. Ideally, the system should also support features like Open Database Connectivity.
- System issues: The data mining system should be compatible with one or more operating systems. Certain structures also provide web-based UIs and allow XML data inputs.
- Data mining methodologies: Choose your data mining system based on the functions offered. While some units may be equipped with only one methodology, say classification, others may provide multiple capabilities. Examples include concept description, association mining, clustering, prediction, discovery-driven OLAP analysis, linkage analysis, similarity search, outlier analysis, etc.
- Database or data warehouse systems: You would have to couple your data mining system with a database or a data warehouse to create an integrated and uniform environment fit for information processing. There are different types of coupling available, such as No Coupling, Loose Coupling, Semi tight Coupling, and Tight Coupling.
- Scalability: Scalability of database size (row) and dimension (column) emerges as yet another significant aspect of a data mining system. When the number of rows goes up by ten times, and the system takes no more than ten times to execute a query, it is considered row scalable. On the other hand, a mining system can be assessed as column scalable if there is a linear increase in the query execution time as more columns are added.
- Visualization tools: The choice of a data mining system would also take its visualization competencies into account. The capacities can range from data visualization to the mining process and result visualization.
- User interface: A user-friendly graphical interface is essential for interactive data mining. While relational database systems may require the use of query languages, the same does not hold for data mining systems.
Technology Trends in Data Mining
- Scalable and interactive data mining methods: Added controls in the form of specifications and constraints can guide data mining systems in not only effectively handling huge volumes of data but also searching for interesting patterns.
- Standardization of query languages: Standard querying languages will improve interoperability between different data mining functions and promote systematic development of solutions.
- Visual data mining: Visual data mining has picked up pace as one of the top data mining trends, presenting innovative opportunities for knowledge discovery.
- Research analysis: Data mining applications are not limited to the tech world. Data cleaning, preprocessing, visualization, and integration of databases have transformed the broad field of research.
- Web mining: Web content mining, web log mining, and other mining services on the internet have secured a place among the flourishing subfields of data mining.
- Multi-database and distributed data mining: Multidatabase data mining analyzes patterns across multiple databases. Whereas distributed data mining searches data from several network locations.
- Real-time data mining: Real-time data or ‘stream data’ is generated from web mining, mobile data mining, e-commerce, stock analysis, etc. This type of data requires dynamic data mining models.
- Privacy protection and information security have also come to light as a notable trend in the data mining space.
In this blog, we understood various data mining applications and explored emerging trends in this sphere.
If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science.
Latest posts by Rohit Sharma (see all)
- Type Conversion & Type Casting in Python Explained with Examples - February 19, 2020
- Top 10 Data Visualization Types: How To Choose The Right One? - February 19, 2020
- K Means Clustering in R: Step by Step Tutorial with Example - February 17, 2020