Nowadays the World Wide Web has becoming one of the most comprehensive information resources. It probably, if not always, covers the information need for any user. Those differences make it challenging to fully use Web information in an effective and efficient manner. Web mining is the application of data mining techniques to extract knowledge from web data including web documents, hyperlinks, ...

Explosive growth in geospatial data and the emergence of new spatial technologies emphasize the need for automated discovery of spatial knowledge. Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial databases. The complexity of spatial data and intrinsic spatial relationships limits the usefulness of convention...

Data mining is a non-trivial process. That is identifying novel, valid and potentially useful patterns in data. Data mining supports automatic data exploration. That is extracting hidden information from the huge database. Data mining refers to search useful and relevant information from the database. Spatial mining is a branch of data mining. The spatial mining deals with the location or geo-r...

Spatial data mining practice for the extraction of useful information and knowledge from massive and complex spatial database. Most research in this area has focused on efficient clustering algorithm for spatial database to analyze the complexity. This paper introduces an active spatial data mining approach that extends the current spatial data mining algorithms to efficiently support user-defi...

due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. association rules mining is one of the data mining techniques to extract useful patterns in the form of association rules. one of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. hiding the as...

Spatial data mining is the process of discovering, motivating and previously unknown, but potentially helpful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more tricky than extracting the parallel patterns from established numeric and definite data due to the complexity of spatial data types, spatial relationships, and spatial autocorr...

The constant increase in use of geographic data in different application domains has resulted in large amounts of data stored in spatial databases and in the desire of data mining. Many solutions for spatial data mining have been proposed. Most create data mining languages or extend existing query languages to support data mining operations. This paper presents an interoperable framework for sp...

this paper presents a survey on soil pb concentration around sepahanshahr town located in vicinity of isfahan. due to the lack of regulation and environmental education and awareness, sepahanshahr is now a rapid growing residential area suffering from the considerable consequences of poorly regulated mining activities operating in its vicinity. the aim of this study is to explore the spatial st...

Although uncertainties exist in spatial data mining, they have not been paid much attention to. Uncertainty may have an influence on the confidential level, supportable level, and interesting level of spatial data mining. This paper proposes uncertainty-based spatial data mining. First, the concept is given in the integrated contexts of both uncertainty and spatial data mining. The inherent unc...

The importance of spatial data mining is growing with the increasing incidence and importance of large spatial datasets repositories of remote-sensing images, location based mobile app data, satellite imagery, medical data and crime data with location information, three dimensional maps, traffic data and many more. However, as classical data mining techniques are often inadequate for spatial da...