Krzysztof Koperski

Discovery of Spatial Association Rules in Geographic Information Databases

Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data- and knowledge-bases. In this paper, an efficient method for mining strong spatial association rules in geographic information databases is proposed and studied. A spatial association rule is a rule indicating certain association relationship among a set of spatial and possibly some nonspatial predicates. A strong rule indicates that the patterns in the rule have relatively frequent occurrences in the database and strong implication relationships. Several optimization techniques are explored, including a two-step spatial computation technique (approximate computation on large sets, and refined computations on small promising patterns), shared processing in the derivation of large predicates at multiple concept levels, etc. Our analysis shows that interesting association rules can be discovered efficiently in large spatial databases.

GeoMiner: a system prototype for spatial data mining

Spatial data mining is to mine high-level spatial information and knowledge from large spatial databases. A spatial data mining system prototype, GeoMiner, has been designed and developed based on our years of experience in the research and development of relational data mining system, DBMiner, and our research into spatial data mining. The data mining power of GeoMiner includes mining three kinds of rules: <italic>characteristic rules, comparison rules</italic>, and <italic>association rules</italic>, in geo-spatial databases, with a planned extension to include mining <italic>classification rules</italic> and <italic>clustering rules</italic>. The <italic>SAND</italic> (<italic>Spatial And Nonspatial Data</italic>) architecture is applied in the modeling of spatial databases, whereas GeoMiner includes the <italic>spatial data cube construction module</italic>, <italic>spatial on-line analytical processing</italic> (<italic>OLAP</italic>) <italic>module</italic>, and <italic>spatial data mining modules</italic>. A spatial data mining language, GMQL (<italic>Geo-Mining Query Language</italic>), is designed and implemented as an extension to <italic>Spatial SQL</italic> [3], for spatial data mining. Moreover, an interactive, user-friendly data mining interface is constructed and tools are implemented for visualization of discovered spatial knowledge.

Object-based selective materialization for efficient implementation of spatial data cubes

With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and OLAP of spatial data. In this paper, we study methods for spatial OLAP, by integrating nonspatial OLAP methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for the computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies being proposed, including approximation and selective materialization of the spatial objects resulting from spatial OLAP operations. The focus of our study is on a method for spatial cube construction, called object-based selective materialization, which is different from cuboid-based selective materialization (proposed in previous studies of nonspatial data cube construction). Rather than using a cuboid as an atomic structure during the selective materialization, we explore granularity on a much finer level: that of a single cell of a cuboid. Several algorithms are proposed for object-based selective materialization of spatial data cubes, and a performance study has demonstrated the effectiveness of these techniques.

Selective Materialization: An Efficient Method for Spatial Data Cube Construction

On-line analytical processing (OLAP) has gained its popularity in database industry. With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and on-line analytical processing of spatial data. In this paper, we study methods for spatial OLAP, by integration of nonspatial on-line analytical processing (OLAP) methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies proposed, including approximation and partial materialization of the spatial objects resulted from spatial OLAP operations. Some techniques for selective materialization of the spatial computation results are worked out, and the performance study has demonstrated the effectiveness of these techniques.

Learning bayesian classifiers for scene classification with a visual grammar

A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.

Mining multiple-level spatial association rules for objects with a broad boundary

Abstract Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding field since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment and planning. The collected data far exceeds peoples ability to analyze it. Thus, new and efficient methods are needed to discover knowledge from large spatial databases. Most of the spatial data mining methods do not take into account the uncertainty of spatial information. In our work we use objects with broad boundaries, the concept that absorbs all the uncertainty by which spatial data is commonly affected and allows computations in the presence of uncertainty without rough simplifications of the reality. The topological relations between objects with a broad boundary can be organized into a three-level concept hierarchy. We developed and implemented a method for an efficient determination of such topological relations. Based on the hierarchy of topological relations we present a method for mining spatial association rules for objects with uncertainty. The progressive refinement approach is used for the optimization of the mining process.

VisiMine: interactive mining in image databases

We describe VisiMine, a system for data mining and statistical analysis of large collections of remotely sensed images.

Image information mining utilizing hierarchical segmentation

The hierarchical segmentation (HSEG) algorithm is an approach for producing high quality, hierarchically related image segmentations. The VisiMine image information mining system utilizes clustering and segmentation algorithms for reducing visual information in multispectral images to a manageable size. The project discussed herein seeks to enhance the VisiMine system through incorporating hierarchical segmentations from HSEG into the VisiMine system.

Extracting statistical data frames from text

We present a framework that bridges the gap between natural language processing (NLP) and text mining. Central to this is a new approach to text parameterization that captures many interesting attributes of text usually ignored by standard indices, like the term-document matrix. By storing NLP tags, the new index supports a higher degree of knowledge discovery and pattern finding from text. The index is relatively compact, enabling dynamic search of arbitrary relationships and events in large document collections. We can export search results in formats and data structures that are transparent to statistical analysis tools like S-PLUSID®. In a number of experiments, we demonstrate how this framework can turn mountains of unstructured information into informative statistical graphs.

Interactive training of advanced classifiers for mining remote sensing image archives

Advances in satellite technology and availability of downloaded images constantly increase the sizes of remote sensing image archives. Automatic content extraction, classification and content-based retrieval have become highly desired goals for the development of intelligent remote sensing databases. The common approach for mining these databases uses rules created by analysts. However, incorporating GIS information and human expert knowledge with digital image processing improves remote sensing image analysis. We developed a system that uses decision tree classifiers for interactive learning of land cover models and mining of image archives. Decision trees provide a promising solution for this problem because they can operate on both numerical (continuous) and categorical (discrete) data sources, and they do not require any assumptions about neither the distributions nor the independence of attribute values. This is especially important for the fusion of measurements from different sources like spectral data, DEM data and other ancillary GIS data. Furthermore, using surrogate splits provides the capability of dealing with missing data during both training and classification, and enables handling instrument malfunctions or the cases where one or more measurements do not exist for some locations. Quantitative and qualitative performance evaluation showed that decision trees provide powerful tools for modeling both pixel and region contents of images and mining of remote sensing image archives.