Knowledge Mining Using Surveys

Data mining is the process of discovering patterns in large datasets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to periscope information (with intelligent methods) from a dataset and transform the information into a comprehensible structure for remoter use. Data mining is the wringer step of the "knowledge discovery in databases" process, or KDD. Aside from the raw wringer step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data wringer and data mining is that data wringer is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the value of data, whereas data mining uses machine learning and statistical models to uncover underhand or subconscious patterns in a large volume of data.

The term "data mining" is in fact a misnomer, considering that the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It is also a buzzword and is wontedly unromantic to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any using of computer visualization support system, including strained intelligence (e.g., machine learning) and merchantry intelligence. Often the increasingly unstipulated terms (large-scale) data wringer and analytics - or, when referring to very methods, strained intelligence and machine learning - are increasingly appropriate.

The very data mining task is the semi-automatic or will-less wringer of large quantities of data to periscope previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in remoter wringer or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain increasingly well-judged prediction results by a visualization support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do vest to the overall KDD process as spare steps.

The related terms "data dredging," "data fishing," and "data snooping" refer to the use of data mining methods to sample parts of a larger population dataset that are (or may be) too small for reliable statistical inferences to be made well-nigh the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test versus the larger data populations.

In view of the tremendous production of computer data worldwide, there is a strong need for new powerful tools that can automatically generate useful knowledge from a variety of data, and present it in human-oriented forms. In efforts to satisfy this need, researchers have been exploring ideas and methods ripened in machine learning, statistical data analysis, data mining, text mining, data visualization, pattern recognition, etc. A multi-strategy methodology for an emerging research direction, tabbed knowledge mining, is a tool by which we midpoint the derivation of high-level concepts and descriptions from data through symbolic reasoning involving both data and relevant preliminaries knowledge. The constructive use of preliminaries as well as previously created knowledge in reasoning well-nigh new data makes it possible for the knowledge mining system to derive useful new knowledge not only from large amounts of data, but also from limited and weakly relevant data.