The module Knowledge Discovery in Databases II covers advanced techniques to handle large data volumes, volatile data streams, complex object descriptions and linked data.
These topics are also known as the three major challenges (Volume, Velocity, Variety) in Big Data Analysis.
As data mining is a process, the definition will include a number of interpretations of the process.
“Data mining is the extraction of implicit, previously unknown, and potentially useful information from data.
In other words in the traditional data set the values of each object are supposed to be independent from other objects in the same data set, whereas the spatial dataset tends to be highly correlated according to the first law of geography.
The spatial outlier detection is one of the most popular spatial data mining techniques which is used to detect spatial objects whose non-spatial attributes values are extremely different from those of their neighboring objects.The spatial parameters taken into our consideration are; distance, cost, and number of direct connections between neighboring objects.A new model to detect spatial outliers is also presented based on the new definition of the spatial neighborhood relationship.It can lead the way and skill and deep understanding can follow.At least, I have used this to drive much of my work.In fact, new research areas are emerging in this direction, known as bioimage informatics and computational pathology, which are areas basically attempting to apply different methods of image processing, pattern recognition, machine learning and data mining, in multimodal biomedical databases.However, the proposed tools and methods for image collection analysis have some research challenges coming with deluge of big data in biomedicine such as: visual appearance variability, semantic gap between image content and high-level meaning, structural and interpretable representation of image content, semantic inclusion of multimodal information sources, and scalability support with the increasing volume of databases.Intelligent geographic information system (IGIS) is one of the promising topics in GIS field.It aims at making GIS tools more sensitive for large volumes of data stored inside GIS systems by integrating GIS with other computer sciences such as Expert system (ES) Data Warehouse (DW), Decision Support System (DSS), or Knowledge Discovery Database (KDD).In order to solve it, the proposed methodology has three main stages: part-based bioimage representation, semantic bioimage representation and biomedical knowledge discovery.Each stage of methodology state-of-the-art methods from computer vision, image processing, machine learning and data mining will be explored to provide interpretable learning methods supported by high-performance computing.