For meeting the challenge of multi-scale and uncertainty of spatial data, a new theory of building Geo-ontology, which is based on Stratified Rough Sets, is put forward in this paper. The theory for building Geo-ontology based on Stratified Rough Set, called GOSR, studies the Geo-ontology from two aspects: the intension and the extension of the ontology concept.By extending the only one equivalence relation of the rough set to more than two equivalence relations, we consider a nested sequence of m equivalence relations:In conclusion, Geo-Ontology based on Stratified Rough Sets has these characters: a series nested equivalence relation forms a granule different partial ordered lattice. An equivalence relation corresponds to a universe. The elements in the universe are the rough objects in the same spatial scale. The lowest universe is built up by the atom spatial objects. The elements in the higher universe can be built up by generalizability or combination of the lower universe. In this way, we can associate the universes of the Stratified Rough Sets with the multi-scale, uncertain spatial data. On the other hand, the equivalence classes defined by different equivalence relations correspond to different semantic granular Geo-Ontology conception, describing the semantic intension of the spatial data.
With the breakthrough development of a series of technologies such as machine learning and internet of things, data plays an increasingly important role in everyone’s daily affairs and work. With the explosive exponential growth of data volume, data types are also quietly changing, from the traditional single, structured data to today’s diverse, semi-structured data. In the face of emerging new massive data, data mining technology has gradually become the focus of attention. Data mining was also known as knowledge discovery (KDD) at that time. It was mainly defined as a pattern hidden in massive data, which must be understood by people and bring potential benefits. In this paper, we mainly studies the basic principle and algorithm knowledge of data mining, and applies ridge regression and random forest algorithm model in real estate price forecasting. Finally, through the stacking thinking of ensemble learning in ensemble learning, we propose a fusion method of ridge regression and random forest model, and obtain a more accurate and stable prediction model.
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