Network-based Models and Algorithms in Data Mining and Knowledge Discovery

5 Concluding Remarks In this chapter, we have addressed several issues regarding the use of network-based mathematical programming techniques for solving various problems arising in the broad area of data mining. We have pointed out that applying these approaches often proved to be effective in many applications, including biomedicine, finance, telecommunications, etc. In particular, if a real-world massive dataset can be appropriately represented as a network structure, its analysis using standard graph-theoretical techniques often yields important practical results. However, one should clearly understand that the success or failure of applying a certain methodology essentially depends on the structure of the considered dataset, and there is no “universal recipe” that would allow one to obtain useful information from any type of data. This indicates that despite the availability of a great variety of data mining techniques and software packages, choosing an appropriate method of the analysis of a certain dataset is a non-trivial task. Moreover, as technological progress continues, new types of datasets may emerge in different practical fields, which would lead to further research in the field of data mining algorithms. Therefore, developing and modifying mathematical programming approaches in data mining is an exciting and challenging research area for years to come.