Technologies Used in Data Mining

Pre-requisites: Data Mining Techniques

Data mining has incorporated many techniques from other domain fields like machine learning, statistics, information retrieval, data warehouse, pattern recognition, algorithms, and high-performance computing. Since it is a highly application-driven domain, the interdisciplinary nature is typically very significant. Research and development in data mining and its applications prove quite useful in implementing it. We will see major technologies utilized in data mining.

 

Machine Learning: 

It has a main research area that focuses on computer programs that will automatically learn based on the given input data and make intelligent decisions. There are similarities and interrelations between machine learning and data mining. For classification and clustering approaches, machine learning is often applied to predict accuracy. Typical machine learning problems that are utilized in mining are:

1. Supervised learning that makes use of class labels to predict information
2. Unsupervised learning doesn’t use class labels similar to clustering but it will discover new classes within data.
3. Semi-supervised learning will redefine the boundaries between two classes and makes use of both labeled and unlabeled examples.
4. Active learning will ask the user to label the classes that may be from unlabeled examples. It will optimize learning by acquiring data from the user.

Information Retrieval: 

The technique searches for the information in the document, which may be in text, multimedia, or residing on the Web. It has two main characteristics:

1. Searched data is unstructured
2. Queries are formed by keywords that don’t have complex structures.

The most widely used information retrieval approach is the probabilistic model. Information retrieval combined with data mining techniques is used for finding out any relevant topic in the document or web.

Uses: A large amount of data are available and streamed in the web, both text and multimedia due to the fast growth of digitalization including the government sector, health care, and many others. The search and analysis have raised many challenges and hence Information Retrieval becomes increasingly important.

Statistics:

Data mining has an inherent connection with statistics. It studies the collection, and interpretation performs the analysis and helps visualize data presentation. A statistical model is used for data classes and data modeling. It describes the behavior of an object in a class and its probability. Statistical models are the outcomes of data mining tasks like classification and data characterization. Or we can use the mining task on top of the statistical models.

Advantage:

• Statistics can be used to model noise and missing data values. The tools for forecasting, predicting, or summarizing data can be availed by statistics. Statistics are useful for pattern mining. After mining a classification model, the statistical hypothesis is used for verification. A hypothetical test makes the decisions using the test data. The result is statistically significant if it is not likely to have been incurred by chance.

Disadvantage:

• When the statistical model is used on large data set, it increases the complexity cost. When data mining is used to handle large real-time and streamed data, computation costs increase dramatically. 

Database System & Data warehouse:

Database systems are used in query languages, query processing, optimization, and data models. Recent database system data analytics capabilities that use data mining and warehousing techniques. Data warehousing combines data from multiple sources (heterogeneous) and gathers historical data in various timeframes.  It facilitates data cubes in a multidimensional database. The OLAP facilitates a multi-dimensional database. The data mining task is used to extend the existing requirement of the database system that would enhance the capabilities and enhance users’ sophisticated requirements