Data Mining Trends and Applications

Data Mining is the process of extracting relevant information from large sets of raw data. Some data mining techniques are:

• Pictorial Data Mining
• Text Mining
• Social Media Mining
• Web Mining
• Audio and video mining

The term data mining is also referred to as Knowledge Discovery in Data (KDD). Data mining includes data collection and warehousing using mathematical algorithms.

Key features of data mining

• Predicting the pattern based on behavioural analysis and recent trend
• Data visualisation and creation of relevant information
• Dealing with complex data sets and large databases
• Identification of unusual trends or behaviours in the marketplace

Data mining includes lots of applications which could be utilised by companies from various sectors. Data mining applications include:

• Financial Data Analysis (Cash Flow Analysis, Customer Credit Policy Analysis, Prediction of Payments, Investigation of Financial Crimes)
• Market Basket Analysis (Understanding the purchasing patterns and buying behaviours of consumers in the retail industry involving the price comparison among stores)
• Segmentation and Targeting (Data mining enables you to divide your target audience into groups and tailor your service according to their specific needs and expectations. In this way, you can ensure customer satisfaction in your business)
• Fraud Detection (Gathering the fraudulent records by converting data into information)
• Manufacturing (When it comes to engineering a product, prediction becomes essential to estimate the lifecycle of a product, the expected number of defects for quality control in production systems)

In our contemporary world, the digital age comes up with a variety of data mining applications. These are sectors where data mining applications are used:

• Education
• Engineering
• Finance
• Government
• Marketing
• Police
• Retail
• Science
• Telecommunications

Data mining is a topic where continuous improvements are being observed. Thereafter, it would be better to stay updated for upcoming trends. Here are future trends in data mining:

• Collective Data Mining

Most of the time, data mining is implemented by putting an emphasis on either a single database or the data warehouse where the relevant information is located. On the other hand, data might be stored at multiple locations. In such a circumstance, Collective Data Mining, in other words, Distributed Data Mining is effective to reach different databases and combine them.

• Sequence & Time Series Data Mining

Another upcoming trend in data mining is based on extracting the information from sequential data which could be ordered by time or another criterion. In these instances, Sequence Data Mining, or Time Series Data Mining would be the common approach to identify patterns and expected trends. This allows companies to track and estimate the demand pattern for production or services so that high levels of customer satisfaction can be achieved.

• Phenomenal Data Mining

A trend in data mining is called Phenomenal Data Mining which concentrates on the relationship between the phenomena and data. To illustrate, invoices or cash receipts are possible sources to process data for gathering information based on demographics and purchasing habits of consumers, even companies. The trend comes up with a challenge which requires coding to utilise this feature efficiently.

• Ubiquitous Data Mining

Much has been said about trends in data mining. However, perhaps the most jeopardous one is Ubiquitous Data Mining since it is based on mining data from mobile devices to reach the information. The hazard especially comes through privacy issues as well as complexity and the cost. The complexity is derived from data management in a mobile environment. Advanced algorithms are needed to overcome this problem. Yet, it is still one of the most promising methods in data mining.

• Multimedia Data Mining

One of the trends in data mining is called Multimedia Data Mining which encapsulates text mining and hypermedia mining. The data types that could be analysed through this approach include images, audio, video and even animation. Similarity research, clustering, association and classification.

• Spatial and Geographic Data Mining

Have you ever considered to combine technology with nature? Spatial and Geographic Data Mining infers information about natural resources, orbiting satellites, and astronomical data. Considering the spatial data, containing distance and topological information makes it unique compared to other forms of data mining. Applications of Spatial and Geographic Data Mining are remote sensing, navigation, and medical imaging.