Data Mining - Mehmed Kantardzic [11]
1. 61% of managers believe that information overload is present in their own workplace,
2. 80% believe the situation will get worse,
3. over 50% of the managers ignore data in current decision-making processes because of the information overload,
4. 84% of managers store this information for the future; it is not used for current analysis, and
5. 60% believe that the cost of gathering information outweighs its value.
What are the solutions? Work harder. Yes, but how long can you keep up when the limits are very close? Employ an assistant. Maybe, if you can afford it. Ignore the data. But then you are not competitive in the market. The only real solution will be to replace classical data analysis and interpretation methodologies (both manual and computer-based) with a new data-mining technology.
In theory, most data-mining methods should be happy with large data sets. Large data sets have the potential to yield more valuable information. If data mining is a search through a space of possibilities, then large data sets suggest many more possibilities to enumerate and evaluate. The potential for increased enumeration and search is counterbalanced by practical limitations. Besides the computational complexity of the data-mining algorithms that work with large data sets, a more exhaustive search may also increase the risk of finding some low-probability solutions that evaluate well for the given data set, but may not meet future expectations.
In today’s multimedia-based environment that has a huge Internet infrastructure, different types of data are generated and digitally stored. To prepare adequate data-mining methods, we have to analyze the basic types and characteristics of data sets. The first step in this analysis is systematization of data with respect to their computer representation and use. Data that are usually the source for a data-mining process can be classified into structured data, semi-structured data, and unstructured data.
Most business databases contain structured data consisting of well-defined fields with numeric or alphanumeric values, while scientific databases may contain all three classes. Examples of semi-structured data are electronic images of business documents, medical reports, executive summaries, and repair manuals. The majority of Web documents also fall into this category. An example of unstructured data is a video recorded by a surveillance camera in a department store. Such visual and, in general, multimedia recordings of events or processes of interest are currently gaining widespread popularity because of reduced hardware costs. This form of data generally requires extensive processing to extract and structure the information contained in it.
Structured data are often referred to as traditional data, while semi-structured and unstructured data are lumped together as nontraditional data (also called multimedia data). Most of the current data-mining methods and commercial tools are applied to traditional data. However, the development of data-mining tools for nontraditional data, as well as interfaces for its transformation into structured formats, is progressing at a rapid rate.
The standard model of structured data for data mining is a collection of cases. Potential measurements called features are specified, and these features are uniformly measured over many cases. Usually the representation of structured data for data-mining problems is in a tabular form, or in the form of a single relation (term used in relational databases), where columns are features of objects stored in a table and rows are values of these features for specific entities. A simplified graphical representation of a data set and its characteristics is given in Figure 1.4. In the data-mining literature,