Information Technology and Automation have significantly raised the vigorous competition among companies. Not only can companies today generate and collect big data but also simultaneously store and organize it. The Database Management System (DBMS) allows companies to extract valuable information from it delivering the companies’ goals. This process is called data mining. Data mining acts as a support system to help facilitate effective organization functioning. Because of its tremendous potential, data mining has been attracting attention and companies have managed to extract and develop patterns and trends from the database.
Data Mining Process
Harvesting this valuable information from the database requires different software. But looking at its core, the process of data mining is one of knowledge discovery – Knowledge Discovery in Data or KDD. The process involves the following phases:
- At the problem definition stage, the task is to understand the goals and objectives that the organization has set for itself. In addition, companies try to identify the core problems they are struggling with. The understanding of the business is translated into objectives that can be achieved.
- Once the problem has been defined, companies begin collecting data that is then analysed to identify the quality of the problems. The data is explored with traditional analytical tools including Statistics.
- Data that has been collected then undergoes cleaning and transformation for the modelling process. Data transformation takes place where it is treated to aggregation, normalization, generalization, attribute construction, smoothing, etc.
- The modelling process evaluates the data with the use of modelling techniques and mathematical tools. This process helps achieve an optimal value leading to the preparation of a high-quality model.
- When data mining experts begin evaluating the model, they observe patterns in the data that can be for or against the goals and objectives of the organization. Data mining experts also ensure that the model takes into account all the issues that the business is dealing with.
- Finally, the result of the data mining process is presented to the company in the form of spreadsheets and other operational tools.
Data Mining Techniques
When data is mined, the technique chosen is of great importance to deliver the desired results. The selection depends on the business itself, its nature, and the issues faced by it. Some common techniques used in data mining include:
- Classification: The classification technique allows for simple classification of data into groups. It is one of the most commonly used techniques in data mining and has two specializations namely neural network and tree decisions.
- Clustering: Clustering differs from classification in that objects with similar characteristics and attributes are put together in a class. This is achieved through automation.
- Sequential Pattern: During data mining, the discovery of sequential patterns is done by observing the most repetitive behaviours. The data which show these patterns are extracted.
- Outer Detection: Elements or data items which do not follow the general dataset model are detected through this technique. It is also referred to as outlier mining or analysis. The technique is particularly useful in detecting faults, frauds, and intrusion.
- Regression: This statistical tool helps to identify and analyse the relationships existing between variables.
- Prediction: Prediction techniques help in predicting the future events by analysing the past trends. These techniques facilitate causal links and relationships between variables, trends, and classify and match them according to patterns.
- Association Rules: These rules help find a co-relation between the buying behaviours and patterns of a customer with every transaction. For this reason, this tool is often used when companies want to learn about customers’ buying behaviours.
In addition to these techniques, data mining also uses a number of tools. These include Oracle data mining, Orange, Rapid Miner, Weka, R, Rattle, Apache Mahout, and Rattle.
Benefits and Challenges in Data Mining
Data mining provides great opportunities for various sectors including the public sector, healthcare, retail, telecommunication, transportation, manufacturing, and finance. These sectors traditionally deal with huge data. Data mining helps in analysing the huge input of data and improves predictions. In addition, the data mining tools can help detect fraud and faults while bringing to light hidden patterns and trends. The complex data is, therefore, resolved into understandable units for improvements.
However promising, data mining has its own set of challenges. Extracting useful information from complex data is difficult and challenging. Data mining for biological and environmental issues are prone to ethical overstepping. And, the integrity, privacy, and security of data is always a big concern. It is also difficult to assess the scalability of big data owing to its grand nature.
A Career in Data Mining
Data mining professionals are in huge demand today and more companies are looking for experts who can decode the big data. To become a data mining expert, one has to develop a thorough understanding of the dynamics of data and keep abreast of technology.
Various courses help professionals develop this insight.
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