Data Mining: Uses, Techniques, Tools, Process & Advantages

Do you think data mining is about digging out data? It’s just data scraping or extraction. However, its name sounds puzzling. Indeed, it’s not the scraping of emails, sales, address, contact details or any other information.

Do you want to know what it is actually? Let’s move on:

What is data mining?

Data mining is actually the process of deriving patterns, groups or clusters of data. Then, the data chunks are filtered. Most relevant chunks are combed through. And eventually, what we get is the relevant information (not the data).

Data is a set of information. On the flip side, information illustrates the value or meaning of the data sets.

Let’s say, around 25 brands of handsets are prevalent in the US. A new brand, before making its first impression in the market, required information. It outsourced an organization for data mining. That organization dug out figures of all selling phones. Various resources were exploited. Eventually, the collected data was put together. Deep analysis placed iPhone as the most selling handsets. And blackberry trailed it.

So, the new brand got the gigantic groups of handset manufacturers. Making the projections became a walkover after the analysis of a given example of data mining. Subsequently, the organization could derive new intelligence.

Data Mining

Why do organisations employ data mining?

Data mining extracts patterns, classes, groups or clusters underlying the set of data. This extremely useful method computes:

  • Relational databases
  • Data warehouses
  • Advanced DB and information repositories
  • Object-oriented and object-relational databases
  • Transactional and spatial databases
  • Multimedia and streaming databases
  • Text databases
  • Web content

Why do companies employ data mining for their operations? Actually, they are extremely useful for figuring out:

  • What’s the trend?
  • Who are competitors?
  • What are the shortcomings?
  • Where are the loopholes?
  • What can be the effective future business intelligence?
  • What strategies can be introduced for more efficiency?
  • What is the history of fraud?
  • What are the business risks?
  • What can be the best methods of customer satisfaction?

All these and many more business intelligences can be derived from it. It’s really a vast concept. The future can be changed with a revolutionary idea. And data mining breeds it via data analysis. It formulates predictive analysis. Later on, their execution sets as a trend.

What are data mining methods?    

Mining of data basically circumambulates knowledge discovery in databases.  The data mining company works on warehousing over the internet. It maintains its own repositories. Why does it do so?

Well, the information can be needed anytime. Its game changing attribute make it extremely powerful. At any point of time, that information can unearth loopholes. It can also unveil most profitable patterns. Their revelation would, therefore, put the derailed boogie of any business back on the track.

The expert data miners employ any of these techniques to prep up the data for analysis.

1. Classification: This mining process segregates various classes. Subsequently, the data of same class are grouped together.

Let’s say, agriculture production is to be evaluated. The production occurred in cultivation, livestock farming and aquaculture will define various classes. The overall production in the specified classes will be segregated. Thereafter, the production will be evaluated. Analysis will be driven; Predictive strategies will be drafted.

2. Clustering: It defines the method of grouping similar attributes, customers, and their behavior and so on together.

In this example, the organization wants to reward the best performer in each department. Therefore, it would create a cluster of the best performers. The work-efficiency of each one would be walked through. Eventually, the top-guns in each department would be extracted. They would be collated together to form a cluster.   

3. Association Rules: It discovers the probability of co-occurring data. Basically, this method computes the transactional values. For example, a web developer puts the data validation in a form. If the user enters ‘0’ value in the field of amount, the popup will notify. And if the entered value is more than ‘0’, the user will be qualified to proceed.

4. Regression:  This data mining method determines the predictive numeric value of the given data-set. First, the data sets are filtered through deep analysis. Their relationships are tapped. Eventually, the analysts determine likelihood of a specific variable amid other ones.

Consider this example wherein population correlates with food and other requirements.

5. Outer Detection: Typically, it’s a trick to observe the odd pattern or unexpected behaviour. A keen observation helps in screening the datasets that don’t match. Thereby, this data mining technique outlines fraud, intrusion, or fault through data. It’s also known as outlier analysis or outlier mining.

6. Sequential Patterns: It helps you identify which data patterns perfectly match. It filters out the trends and patterns in transactions of a particular span.

For example- an eCommerce website scans the shopping pattern of a particular customer. Let’s say A searches moisturizing lotion, serums and creams during the last half of a year. A thorough observation through the lens of an analyst detects the demand of ‘moisturizer’ by A. Thereby, the strategy-makers use this sequential pattern as a clue to target him later.   

7. Prediction:  It’s all about perceiving what’s going to happen. It can be a trend or a fashion or a fraud. Let’s say, you book a flight to the US. But, they don’t have the idea of whether or not do you pay off the flight charges. So, the data analysts filter previous transactions of that person. Check if he has a clear account (means ‘no’ credit at all). Finally, his transaction is cleared.

What are online data mining tools?

Data mining technique involves utilizing several algorithms, statistical analysis, artificial intelligence and database systems. Its tools enable the user to automate scraping, streamlining and deriving patterns for predictive analysis. Here is a list of most effective data mining tools available online:

  • R-Programming (Free Software)
  • Orange (Open Source)
  • Weka (Free Software)
  • Rapidminer (Open Source)
  • IBM Cognos (Proprietary Licence)
  • SAS Data Mining ((Proprietary Licence))
  • Dundas (Licenced)
  • TERADATA (Licenced)
  • SSDT (Licenced)
  • KNIME (Open Source)
  • Sisense (Licenced)
  • Apache Mahout (Open Source)
  • Oracle (Proprietary Licence)
  • Rattle (Open Source)

What is data mining process?

As it is foretold, data mining is a handy method of discovering knowledge from the databases. But it’s not just the scraping of data. We can call the mining of the underlying patterns. The data filters from these steps. Eventually, it delivers the untapped prospective strategies.

data mining process

What are the sources of data mining?

Hard copies are gradually converting into soft copies. This is why trillions of data is stored into the data warehouses. Besides, there are some more data repositories. The companies indulged in market research or business research. They use these sources for scraping and mining data through data mining software:

  • Databases, like CRMs of the website
  • Text document, Like Magazines, Newspapers
  • Computer simulation, like Hard Disk
  • Social networks, like Twitter and Facebook.
  • Media, like Internet
  • Interactions, like Survey, Polls, Personal Interview

What are the advantages of mining data?

  1. Marketing/Retail: It is used to predict, analyze and execute refined marketing strategies for more revenue & sale.
  2. Finance/Banking: The financial condition is improvised. Predictive analysis delivers data of invaluable expenses and crediting. Therefore, the user gains proactivity over them. 
  3. Manufacturing: The pre-evaluation of manufacturing data unearths the secrets to reverse the loss-making exercise into profitable actions.
  4. Scalability: The business grabs every strategy to win the lead through predictive patterns of production, sales and marketing.
  5. ORM (Online Reputation Management): The data help to overcome prospective disasters in any domain of the business. Thereby, the predictive strategies are prepared to build up positive reviews & relative advertisements through excellent production and customer support.
  6. Detecting Fraud: By employing outer detection and prediction techniques, data mining taps the patterns that are suspicious and can lead to fraud.
  7. Lead Generation & Visibility: The eCommerce websites win thousands of leads and attract eyeballs. It helps them push up their website ranking. The higher you rank, the closer you get to leads and visibility.
  8. Comparative Analysis: Its various techniques trigger collection of relative data. These sets let the analysts compare similar and dissimilar sets to accomplish comparative data analysis.