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Data Mining in the Era of Big Data and Cloud Computing






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Data Mining in the Era of Big Data and Cloud Computing

 

Thanks to big data and cloud computing, many businesses now use data mining. Data mining refers to the activities of transforming raw data into information, understanding patterns in the information and making decisions with them.

While data mining techniques have been applied for years, it has become more popular than it ever was. The introduction of big data and cloud computing has encouraged broader usage of data mining as organisations now have varied information about their clients and means of storing them at their finger-tips.

For instance, companies now have full information about their clients. They have statistics about how many customers they have; their age group and earning capacities. Also, companies can easily and securely store these delicate pieces of information in the clouds.

 

The level of information at the disposal of companies makes it possible to implement effective and well-informed strategies. They can communicate their business offerings to the segment of their customers that are best suited for such product.

 

Data Mining Techniques

There are different data mining techniques; as much as there are different terms used for them. Here are some of the different data mining techniques:

 

  • Relation

This technique is the most common and the simplest data mining technique. The technique helps you to correlate between different items of the same type to take note of patterns. For instance, eatery owner could track their customer’s buying pattern by identifying that they usually order for a specific drink when buying sandwich. This will help the eatery owner to predict that customers will buy the drink when buying sandwich in the future. Different data mining software can be used to develop a relation-centric data mining tool.

 

  • Grouping

Data mining that is based on grouping technique is used to describe various attributes of an object, item or a customer to create and identify a specific class. A company can easily group customers into different class by identifying attributes such as gender, age and occupation. This makes it easy for the company to add a new customer into the class by considering the identified attributes. In addition, a company can use grouping technique to complement other techniques.

 

  • Decision Trees

The decision trees data mining technique is used as part of the guideline and support for selecting a particular data. Decision trees usually combined with grouping systems.

 

Data Preparation and Implementation

 

Data mining demands that you create a compatible data framework for processing, identifying, and building information. It is important that you arrange information in a suitable manner. Also, identify the variables; the need to create new variables; and the different business needs for data mining.

 

Data Mining Process

 

Five stages make up the data mining process. The first stage is to gather data and feed it into the data warehouse. Secondly, data is stored in the cloud or in-house hardware. Thirdly, analysts and IT professionals are consulted to access and organise the data. Thereafter, data mining software/tool is used to organise data concerning user’s result. Lastly, the user provides an easily-understood data.

 

Data mining requires that the huge amount of data, available to an individual or an organisation, is put to best use. Ensure that you use your data by managing, restructuring and reformatting it regularly, to make value of its meaning.

 

 

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