Knowledge discovery is an interdisciplinary area focusing upon methodologies for extracting useful knowledge from data. The ongoing rapid growth of online data due to the Internet and the widespread use of databases have created an immense need for knowledge discovery techniques.
In general, knowledge discovery can be defined as the process of identifying interesting and new patterns in data. These patterns can include relations, events or trends, and they can reveal both exceptions and regularities. In the core of the process, data mining methods are utilized for extracting and verifying patterns.
Data discovery techniques for extracting knowledge from data can be done by using pattern recognition, machine learning algorithms, data visualization, optimization, and high-performance computing, for delivering advanced business intelligence and web discovery solutions.
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Knowledge discovery is a process that gives a framework for applying various methods, and an ideal knowledge discovery system controls the whole life span from defining the discovery task to utilizing the results. Take a look at the process of knowledge discovery.
Selecting the goals of the knowledge discovery process
Start by determining goals for your data discovery process. The final goal may be to construct a specific tool to be integrated into some product or it can be the overall exploration of the document collection.
Selecting of data for knowledge
In this step, you have to select a set of data objects that are to be used in discovery. Companies can use text retrieval queries for collecting data from multiple, heterogeneous sources. Directing the focus to some specific features at a time can usually help in producing more understandable results.
Data pre-processing
Data has to be transformed into the form that is required by the data mining methods. For instance, a rule discovery method may take as input a line for each data object, and each line has to contain a set of attribute values. The amount of pre-processing required depends on the method used and the quality of the data.
Applying the data mining methods
For the actual process of extraction of patterns, different alternative methods are often available, for example, categorization of tools that may be based on decision trees, neural networks, or rule discovery methods. Also, many general statistical methods can be used.
Features of a knowledge discovery system depend extensively upon the discovery task and also upon the intended user.
Companies can make use of several generic, single-task tools available, that support data mining and machine learning techniques. Thus, using these techniques, companies can interpret accurate solutions from the observed results.
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(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors. All thecontents in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)
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About The Author:
Naveen is currently the CEO at Allerin Tech Pvt Ltd. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. Naveen is a keynote speaker and thought leader in the area of IoT solutions, Machine learning and Block Chain Technology.
Specialties: Solution Design and consultancy , Data Science, Machine Learning, Deep Learning Enterprise Application Planning, Cost Optimization and Block Chain
