5 Important Future Trends in Data Mining

Data mining is one of the most widely used methods to extract data from various sources and organize it for better usage. Despite having different commercial systems for data mining, a lot of challenges surface when they are implemented. With the rapid evolution in the field of data mining, companies are expected to stay abreast with all the new developments.

Complex algorithms form the basis for data mining, as they enable data segmentation. This helps to identify various trends and patterns, variations, and predict the probabilities of various events. The raw data may come in any format and is inherently classified based on the source of the data. So, it is crucial for companies to keep a track of all the latest data mining trends and stay updated to make the most of their organizational data and leapfrog their competitors.

Top 5 Data Mining Trends

Businesses slow in adopting the process of data mining are now catching up with the others. Extracting important information through the process of data mining is widely used to make critical business decisions. Data mining is expected to become as ubiquitous as some of the more prevalent technologies used today. Some of the key trends expected to shape the future of data mining include -

Multimedia Data Mining

This is one of the latest methods that is gaining popularity because of its growing ability to capture useful data accurately. It involves the extraction of data from different kinds of multimedia sources such as audio, text, hypertext, video, images, and more. The data extracted is then converted into a numerical representation in different formats. This method can be used for clustering and classifications, performing similarity checks, and identifying associations.
Ubiquitous Data Mining

This method involves the mining of data from mobile devices to get information about individuals. Despite having several challenges in this type such as complexity, privacy, cost, and more, this method is poised to grow and fins applications in various industries especially in studying human-computer interactions.
Distributed Data Mining

This type of data mining is gaining popularity as it involves the mining of a huge amount of information stored in various company locations or within multiple organizations. Highly sophisticated algorithms are used to extract data from different locations and provide better insights and reports based on them.
Spatial and Geographic Data Mining

This is a new trending type of data mining that includes extracting information from environmental, astronomical, and geographical data, which also includes images taken from the outer space. This type of data mining reveals various aspects such as distance and topology, which is mainly used in geographic information systems and other navigation applications.
Time Series and Sequence Data Mining

The primary application of this type of data mining is the study of cyclical and seasonal trends. This practice is also helpful in analyzing even random events that occur outside the normal series of events. This method is mainly being used by retail companies to assess customers' buying patterns and their behaviors.

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