What is KDD?
‘KDD’ refers to ‘Knowledge Discovery in Databases,’ an extensive process that deals with finding new information from available data with the purpose of analyzing and interpreting this information to make it useful. A huge part of the KDD process is referred to as the data mining process wherein data relevance and relationships are extracted from a large data source. Through data mining, the KDD process is promoted and will eventually yield valuable discoveries of new information.
The KDD process starts with data selection and setting the target to justify the goals of the information discovery process. Once the target data is determined, the pre-processing stage will follow. At this time, all unnecessary details will be filtered out, and strategies will be formulated to ensure that efficient handling is done for data fields that lack the necessary details and information. It is also during this time that some form of accounting is done in terms of time sequencing. After the data pre-processing and cleanup stage, all the information will go through a process of reduction and projection. It is during this portion that all relevant features or information are found, and these will then go through the data mining stage in which the data will either be classified or clustered into groups. The pattern selection and identification of parameters are also done during this stage of the process. When all the patterns and relevant information are extracted, analysis and interpretation of these patterns will then commence from which the new knowledge or discovery is then completed.
KDD is especially useful for various businesses and organizations that basically have volumes of consumer data available but don’t have any idea about how to analyze or interpret it. Through KDD, or data mining, all available data may then be analyzed to give useful information in terms of consumer spending habits and behavior.