Data mining is the process of looking at large banks of information to generate new information. Intuitively, you might think that data “mining” refers to the extraction of new data, but this isn’t the case; instead, data mining is about extrapolating patterns and new knowledge from the data you’ve already collected.

Relying on techniques and technologies from the intersection of database management, statistics, and machine learning, specialists in data mining have dedicated their careers to better understanding how to process and draw conclusions from vast amounts of information. But what are the techniques they use to make this happen?

Data Mining Techniques

1. Track the Patterns

Recognizing the patterns in your dataset is one of the basic techniques in data mining. The data is observed at regular intervals for recognizing some aberration. For example, it can be seen if a particular person travels around different countries, then that person will require to book tickets regularly. Thus a special credit card can be offered.

2. Classification

Classification is a more complex data mining technique that forces you to collect various attributes together into discernable categories, which you can then use to draw further conclusions, or serve some function. For example, if you’re evaluating data on individual customers’ financial backgrounds and purchase histories, you might be able to classify them as “low,” “medium,” or “high” credit risks. You could then use these classifications to learn even more about those customers.

4. Association

Association is related to tracking patterns, but is more specific to dependently linked variables. In this case, you’ll look for specific events or attributes that are highly correlated with another event or attribute; for example, you might notice that when your customers buy a specific item, they also often buy a second, related item. This is usually what’s used to populate “people also bought” sections of online stores.

4. Outlier detection

In many cases, simply recognizing the overarching pattern can’t give you a clear understanding of your data set. You also need to be able to identify anomalies, or outliers in your data. For example, if your purchasers are almost exclusively male, but during one strange week in July, there’s a huge spike in female purchasers, you’ll want to investigate the spike and see what drove it, so you can either replicate it or better understand your audience in the process.

5. Clustering

Clustering is very similar to classification, but involves grouping chunks of data together based on their similarities. For example, you might choose to cluster different demographics of your audience into different packets based on how much disposable income they have, or how often they tend to shop at your store.

6. Regression

Regression, used primarily as a form of planning and modeling, is used to identify the likelihood of a certain variable, given the presence of other variables. For example, you could use it to project a certain price, based on other factors like availability, consumer demand, and competition. More specifically, regression’s main focus is to help you uncover the exact relationship between two (or more) variables in a given data set.

7. Prediction

Prediction is one of the most valuable data mining techniques, since it’s used to project the types of data you’ll see in the future. In many cases, just recognizing and understanding historical trends is enough to chart a somewhat accurate prediction of what will happen in the future. For example, you might review consumers’ credit histories and past purchases to predict whether they’ll be a credit risk in the future.

Data Mining Tools

One doesn’t need the particular latest technologies for performing data mining. It can be done using the latest database systems and simple tools available in any organization. Also, one can create its own tool when the appropriate device is missing. The most popular tool is widely used in the industry are given below:

1. R-Language

This is an open-source tool that is used for statistical computing and graphics. This tool helps in effective data handling and storage facility ad these all features are because of the below techniques:

  • Statistical
  • Classical statistical tests
  • Time-series analysis
  • Classification
  • Graphical Techniques

2. Oracle Data Mining

This tool is popularly known as ODM; it is a part of the Oracle Advanced Analytics Database. This tool helps to analyze data in data warehouses and generates detailed insights that help make predictions. These things help to study customer behaviour; products demand ad thus help in increments of selling opportunities.

Challenges being faced in the implementation of Data mine:

  • Skilled experts are needed to make complex data mining queries.
  • Present models may not fit in the future state’s databases .may not fit future conditions.
  • Difficulties faced in managing large databases.
  • It may be needed to modify business practices to use information that has been uncovered.
  • Heterogeneous databases and information coming globally can result in complex integrated information.
  • Data mining has a prerequisite that data must be diverse in nature. Otherwise, results can be inaccurate.