The Definition of Data Mining and Its Examples

In Computer Skills
The definition of data mining and its examples.

The exploration of data mining in finding latent connections and forecasting oncoming trends has a long chronicle. Data mining is also called as knowledge discovery in databases. Before 1990s, this term were not yet existed. However, the foundation consists of three interrelated disciplines, including;

Data mining technology continues to grow following the potential of huge unlimited data and affordable computing power.

In the past decade, the forward movement in the strength and speed of data processing has reached the point of being able to move beyond manual practices which were relatively boring and time consuming in the data analysis. But now data analyzing can be easy and automatic thanks to data mining technology. The more complicated the data sets are, the greater the possibility to uncover interrelated insights. Many types of companies have been using this data processing technology to find the relationship among all of recorded data including promotion, demographics, risk, pricing, competition, internet things, etc. that affect a certain business model, customer relationships, earnings, and business operations.

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Data Mining Definition

Data Mining is the process of organizing and classifying a huge number data sets to find out patterns and build connections to solve issues by way of data analysis. Data mining software allow individuals or companies to forecast future trends. If interpreted or translated literally, data mining is data gathering.

In other words, it is the process of detecting rarities, patterns and connections between a huge size of data sets to predict results. Using a wide variety of techniques, we can use extracted information to improve revenue, efficiency, cut loss, customer services, risk management, and much more.

Data Mining Parameters

If we talk about data mining process, association rules are made by analyzing data for frequently repeating patterns, and making use of the root and confidence criteria to find the significant relationships within the data. The meaning of root here is the support or, in other words, how often a stuff or value appears in the database, while the meaning of confidence in the data mining process is about how many times the information extracted accurate. Other parameters are classification, grouping, path analysis, and forecasting.

Data Mining Software

Data mining software programs are basically used in researches mostly in the fields of mathematics, cybernetics, marketing, and genetics. The use of a data mining software is to push efficiencies and foresee customer behavior. If businesses use the software properly, they can be different from their competitors by means of the use of predictive analysis.

A type of data mining that is getting widely used recently is web mining. It is commonly used in customer relationship management to integrate information extracted by conventional ways through the web. The main purpose of web mining is to understand customer behavior and to assess how powerful a website is.

Other popular data mining methods are network approaches build upon simultaneous tasks learning to classify patterns, confirming parallel and scalable implementation of data mining algorithms, digging large database, dealing with comparative and complicated data types, and also machine learning.

Advantages of Data Mining

Commonly, the advantages of implementing data mining are to finding out hidden patterns and interlinks in data that can be applied to make useful forecasts that for business improvements.

Data mining advantages specifically vary in contingent with the objectives and fields run. Retailers can dig up customer data to improve lead conversion rates. It’s also useful to make one-to-one marketing strives. The information taken from data gathering and analyzing regarding sales history and customer behavior patterns are best references to prepare and develop worthwhile predictive models to boost up sales, defining new products or services.

Financial institutions basically use data mining software to develop risk models and discover frauds. While large manufacturing companies uses the software to develop their product security, recognizing quality problems, develop supply chain management and improve business operations.

Data Mining Examples

Here are simple data mining examples as your inspirations to improve your marketing strategies and outperform competitors in a certain area.

Machine Learning Based on Rules to Find the Relationship Between Data

This technique used for all large volume of product sales activities. Whatever the type or size of a business, sometimes you need to create a valuable connection between the data you have. The relationships that we might never have expected before. For example, 90% of your customers who buy certain products online also buy any other different products, or they don’t buy other products except a certain product they looked for. Such details allow you to make the right marketing material or create special promotions.

Cluster Analysis (Grouping) to Pinpoint Specific Target Groups

This method allows you to identify specific user groups base on the general criteria found within the database being analyzed. The criteria can be age, address, level of education, and so on. This method is useful in marketing field to segment the database. Besides, it can also be useful to get ideas for promote a certain product or service to the right target market.

Regression Analysis to Forecast Marketing Results

A big dream of marketers is being able to predict future trends. By using data mining techniques in regression analysis allow marketers to improve their change management, studying customer behavior and satisfaction levels and other elements related to the criteria for advertising budgets or other costs.

Detecting Anomalies

In any scale of business size, the owner must handle the consequences of any oversight made by the subordinated, providers, or even customers. Small errors in product purchases or data entry may not paralyze your business, but might be very uncomfortable for you. To prevent database anomalies, anomaly detection is needed. There are some specific software  programs that can handle searches because it is programmed to carry out complex operations in databases containing thousands or even millions of records such as names, addresses, telephone numbers, etc.

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