Research on visualization techniques in data mining

Abstract

Visual data mining can help in dealing with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process, through analysis the results of the information visualization, user can integrate the specialist knowledge with the data mining algorithm. This paper summarizes current visualization methods applied in data mining. Current applications about visual data mining technique are analyzed combining with some national advanced data mining tools. Trends are clarified based on the task and object of visual data mining. In digital information age, the rapid development of networks and modern electronic communications equipment cause data flow growing exponentially. A large number of potentially useful knowledge hides in these sharp-growing data. There are two types of the trend of data: data → data garbage, data → information → knowledge. The key of the finally export of data lies in effective methods of information extraction and knowledge discovery tools. Data Mining is the process of extracting potentially valuable knowledge from a lot of historical data. Data mining can be divided into two categories from the perspective of data analysis: descriptive data mining and predictive data mining. The former describe data in a concise means and provide the fun general nature of data. The latter analyze data, set up one or a group of models and attempt to predict the behavior of new data sets. Visualization is the process of transforming data, information and knowledge into a visual representation, and provides an interface between two information processing system of human and computer. Using effective visual interface can quickly and efficiently deal with large amounts of data to find hidden features, relations, patterns and trends, and can lead to new foresight and more efficient decision- making.