Automatic data analysis using pattern

A variety of methods have been developed by mathematicians and computer scientists to address this task, which has become known as data mining (see Chapter 9, Section 9.8). Fayyad defined and described the term data mining as the nontrivial extraction of impHcit, previously unknown and potentially useful information from data, or the search for relationships and global patterns that exist in databases [16]. In order to extract information from huge quantities of data and to gain knowledge from this information, the analysis and exploration have to be performed by automatic or semi-automatic methods. Methods applicable for data analysis are presented in Chapter 9. 

A third major development which was necessary for the commercial implementation of this instrument, was the further development of the software for data analysis. In the initial work described above, the software was developed to the point where the user could read the sequence from the plots of dye concentration vs. time. However, in order for this method to be useful, it was essential that the software automatically call the sequence. Indeed, this capability was the major impetus behind the development of the method, intended to eliminate the need for laborious manual interpretation of data. The problem of autoanalysis is a nontrivial exercise in pattern recognition. At this time, the autoanalysis software developed at ABI and employed on the commercial instrument is approximately 98% accurate, and further improvements under development are expected to increase the accuracy substantially. 

Figure 4.23. The results of a qualitative analysis of a multiple phase sample. Three crystalline phases are clearly identifiable lithium silicate - Li2Si03, silicon oxide - SiOj (quartz), and a different pol)imorph of silicon oxide - tridymite. A low quality diffraction pattern collected during a fast experiment was employed in this example. The data shown on top were smoothed, the background was subtracted, and the Ktt2 components were stripped before the digitized pattern (shown below the smoothed profile) was obtained using an automatic peak search. Note, that many weak Bragg reflections were missed in the peak search,...

With the explosive growth of data available on the World Wide Web (WWW), discovery and analysis of useful information from the World Wide Web becomes a pa-actical necessity. Data Mining is primarily concerned with the discovery of knowledge and aims to provide answers to questions that people do not know how to ask. It is not an automatic process but one that exhaustively explores very large volumes of data to determine otherwise hidden relationships. The process extracts high quality information that can be used to draw conclusions based on relationships or patterns within the data. 

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