27 Oct Steps of Data Mining Processing
The whole Data Mining Processing cannot be completed in a single step. In other words, you cannot get the required information from the large volumes of data as simple as that. It is a very complex process than we think involving a number of processes. The processes including data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge representation are to be completed in the given order.
Types of Data Mining Processes
Different data mining processes can be classified into two types: data preparation or data preprocessing and data mining. In fact, the first four processes, that are data cleaning, data integration, data selection and data transformation, are considered as data preparation processes. The last three processes including data mining, pattern evaluation and knowledge representation are integrated into one process called data mining.
Data cleaning is the process where the data gets cleaned. Data in the real world is normally incomplete, noisy and inconsistent. The data available in data sources might be lacking attribute values, data of interest etc. For example, you want the demographic data of customers and what if the available data does no
t include attributes for the gender or age of the customers? Then the data is of course incomplete. Sometimes the data might contain errors or outliers. An example is an age attribute with value 200. It is obvious that the age value is wrong in this case. The data could also be inconsistent. For example, the name of an employee might be stored differently in different data tables or documents. Here, the data is inconsistent. If the data is not clean, the data mining results would be neither reliable nor accurate.
Data cleaning involves a number of techniques including filling in the missing values manually, combined computer and human inspection, etc. The output of data cleaning process is adequately cleaned data.
b. Data Integration
Data integration is the process where data from different data sources are integrated into one. Data lies in different formats in different locations. Data could be stored in databases, text files, spreadsheets, documents, data cubes, Internet and so on. Data integration is a really complex and tricky task because data from different sources does not match normally. Suppose a table A contains an entity named customer_id where as another table B contains an entity named number. It is really difficult to ensure that whether both these entities refer to the same value or not. Metadata can be used effectively to reduce errors in the data integration process. Another issue faced is data redundancy. The same data might be available in different tables in the same database or even in different data sources. Data integration tries to reduce redundancy to the maximum possible level without affecting the reliability of data.
Data mining process requires large volumes of historical data for analysis. So, usually the data repository with integrated data contains much more data than actually required. From the available data, data of interest needs to be selected and stored. Data selection is the process where the data relevant to the analysis is retrieved from the database.
Data transformation is the process of transforming and consolidating the data into different forms that are suitable for mining. Data transformation normally involves normalization, aggregation, generalization etc. For example, a data set available as “-5, 37, 100, 89, 78” can be transformed as “-0.05, 0.37, 1.00, 0.89, 0.78”. Here data becomes more suitable for data mining. After data integration, the available data is ready for data mining.
Data mining is the core process where a number of complex and intelligent methods are applied to extract patterns from data. Data mining process includes a number of tasks such as association, classification, prediction, clustering, time series analysis and so on.
The pattern evaluation identifies the truly interesting patterns representing knowledge based on different types of interestingness measures. A pattern is considered to be interesting if it is potentially useful, easily understandable by humans, validates some hypothesis that someone wants to confirm or valid on new data with some degree of certainty.
The information mined from the data needs to be presented to the user in an appealing way. Different knowledge representation and visualization techniques are applied to provide the output of data mining to the users.