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Discovering associations between spatial objects: An ilp application
 In Proceedings of the 11th International Conference on Inductive Logic Programming, volume 2157 of LNCS
, 2001
"... Abstract. In recent times, there is a growing interest in both the extension of data mining methods and techniques to spatial databases and the application of inductive logic programming (ILP) to knowledge discovery in databases (KDD). In this paper, an ILP application to association rule mining in ..."
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Cited by 17 (3 self)
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Abstract. In recent times, there is a growing interest in both the extension of data mining methods and techniques to spatial databases and the application of inductive logic programming (ILP) to knowledge discovery in databases (KDD). In this paper, an ILP application to association rule mining in spatial databases is presented. The discovery method has been implemented into the ILP system SPADA, which benefits from the available prior knowledge on the spatial domain, systematically explores the hierarchical structure of taskrelevant geographic layers and deals with numerical aspatial properties of spatial objects. It operates on a deductive relational database set up by selecting and transforming data stored in the underlying spatial database. Preliminary experimental results have been obtained by running SPADA on georeferenced census data of Manchester Stockport, UK. 1
Mining Scientific Data
, 2001
"... The past two decades have seen rapid advances in high performance computing and ..."
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Cited by 14 (4 self)
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The past two decades have seen rapid advances in high performance computing and
An ILP Method for Spatial Association Rule Mining
 In Working notes of the First Workshop on MultiRelational Data Mining
, 2001
"... Knowledge discovery in spatial databases raises challenging multirelational data mining problems. A promising solution approach comes from the field of inductive logic programming (ILP). In this paper, an ILP method for spatial association rule mining is presented. It benefits from the available ..."
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Cited by 13 (5 self)
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Knowledge discovery in spatial databases raises challenging multirelational data mining problems. A promising solution approach comes from the field of inductive logic programming (ILP). In this paper, an ILP method for spatial association rule mining is presented. It benefits from the available prior knowledge on the spatial domain, systematically explores the hierarchical structure of geographic layers, and deals with numerical aspatial properties of spatial objects. The method has been implemented into the ILP system SPADA which operates on a deductive relational database set up by an initial step of feature extraction from a spatial database. Advantages and limits of the method are illustrated by means of examples taken from an application of SPADA to the spatial data of an Italian province.
A Multistrategy Approach to Relational Knowledge Discovery in Databases
 Machine Learning Journal
, 1996
"... . When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real ..."
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Cited by 13 (9 self)
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. When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on realworld databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (firstorder logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better learning can prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database management system. The ILP algorithm is controlled in a modeldriven way by t...
ILP: Just Do It
, 2000
"... Inductive logic programming (ILP) is built on a foundation laid by research in other areas of computational logic. But in spite of this strong foundation, at 10 years of age ILP now faces a number of new challenges brought on by exciting application opportunities. The purpose of this paper is to int ..."
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Cited by 13 (1 self)
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Inductive logic programming (ILP) is built on a foundation laid by research in other areas of computational logic. But in spite of this strong foundation, at 10 years of age ILP now faces a number of new challenges brought on by exciting application opportunities. The purpose of this paper is to interest researchers from other areas of computational logic in contributing their special skill sets to help ILP meet these challenges. The paper presents five future research directions for ILP and points to initial approaches or results where they exist. It is hoped that the paper will motivate researchers from throughout computational logic to invest some time into "doing" ILP.
Discovering Robust Knowledge from Databases that Change
 DATA MINING AND KNOWLEDGE DISCOVERY
, 1998
"... Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with data. However, databases usually change over time and makemachinediscovered knowledge inconsiste ..."
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Cited by 7 (1 self)
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Many applications of knowledge discovery and data mining such as rule discovery for semantic query optimization, database integration and decision support, require the knowledge to be consistent with data. However, databases usually change over time and makemachinediscovered knowledge inconsistent. Useful knowledge should be robust against database changessothatitisunlikely to become inconsistentafter database changes. This paper defines this notion of robustness in the context of relational databases that contain multiple relations and describes how robustness of firstorder Hornclause rules can be estimated and applied in knowledge discovery.Our experiments show that the estimation approach can accurately predict the robustness of a rule.
Comparative Statistical Analyses Of Automated Booleanization Methods For Data Mining Programs
 City University of New York Graduate Center
, 1999
"... COMPARATIVE STATISTICAL ANALYSES OF AUTOMATED BOOLEANIZATION METHODS FOR DATA MINING PROGRAMS by Susan P. Imberman Advisor: Professor Michael Kress, Professor Bernard Domanski KDD (Knowledge Discovery in Databases) is the automated discovery of patterns and relationships in large databases. Da ..."
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Cited by 4 (3 self)
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COMPARATIVE STATISTICAL ANALYSES OF AUTOMATED BOOLEANIZATION METHODS FOR DATA MINING PROGRAMS by Susan P. Imberman Advisor: Professor Michael Kress, Professor Bernard Domanski KDD (Knowledge Discovery in Databases) is the automated discovery of patterns and relationships in large databases. Data mining is one step in the KDD process. Many data mining algorithms and methods find data patterns using techniques such as neural networks, decision trees, statistical analysis, deviation detection, etc. The Boolean Analyzer is a data mining method that finds dependency rules of the form X # Y. Data is Booleanized with regard to values in a threshold set. That is each data transaction/observation is transformed into a vector of 0's and 1's. Each vector defines a state for that transaction/observation. Vector states can be organized into a state occurrence matrix. From this matrix we can compute a measure of event dependency. A new matrix can be formed called a state linkage matrix...
Exploring the power of heuristics and links in multirelational data mining
 In Foundations of Intelligent Systems (ISMIS
, 2008
"... Abstract. Relational databases are the most popular repository for structured data, and are thus one of the richest sources of knowledge in the world. Because of the complexity of relational data, it is a challenging task to design efficient and scalable data mining approaches in relational database ..."
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Cited by 4 (0 self)
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Abstract. Relational databases are the most popular repository for structured data, and are thus one of the richest sources of knowledge in the world. Because of the complexity of relational data, it is a challenging task to design efficient and scalable data mining approaches in relational databases. In this paper we discuss two methodologies to address this issue. The first methodology is to use heuristics to guide the data mining procedure, in order to avoid aimless, exhaustive search in relational databases. The second methodology is to assign certain property to each object in the database, and let different objects interact with each other along the links. Experiments show that both approaches achieve high efficiency and accuracy in real applications. 1
Mining a Natural Language Corpus for MultiRelational Association Rules
, 1997
"... Association rules are generally recognized as a highly valuable type of regularities and various algorithms have been presented for efficiently mining them in large databases. To the best of our knowledge, the application of these algorithms is so far restricted to databases that consist of a single ..."
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Cited by 4 (2 self)
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Association rules are generally recognized as a highly valuable type of regularities and various algorithms have been presented for efficiently mining them in large databases. To the best of our knowledge, the application of these algorithms is so far restricted to databases that consist of a single relation composed of a set of binary attributes. We describe how these restrictions can be overcome through the combination of the available algorithms with standard techniques from the field of inductive logic programming. We present the algorithm AprioriRel, which extends Apriori [ Agrawal et al., 1996 ] to mine association rules in multiple relations. Whereas in Apriori each example is described by means of a single tuple, in AprioriRel each example is viewed as a separate database with a selection, from multiple relations, of all tuples related to the example. Accordingly, the association rules discovered by AprioriRel may combine information from various relations to statements of th...
Tailoring Representations to Different Requirements
 In Proceedings of the 10th International Conference on Algorithmic Learning Theory (ALT
"... Designing the representation languages for the input and output of a learning algorithm is the hardest task within machine learning applications. Transforming the given representation of observations into a wellsuited language LE may ease learning such that a simple and efficient learning algor ..."
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Cited by 3 (1 self)
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Designing the representation languages for the input and output of a learning algorithm is the hardest task within machine learning applications. Transforming the given representation of observations into a wellsuited language LE may ease learning such that a simple and efficient learning algorithm can solve the learning problem. Learnability is defined with respect to the representation of the output of learning, LH . If the predictive accuracy is the only criterion for the success of learning, the choice of LH means to find the hypothesis space with most easily learnable concepts, which contains the solution. Additional criteria for the success of learning such as comprehensibility and embeddedness may ask for transformations of LH such that users can easily interpret and other systems can easily exploit the learning results. Designing a language LH that is optimal with respect to all the criteria is too difficult a task. Instead, we design families of representations, where each family member is well suited for a particular set of requirements, and implement transformations between the representations. vvIn this paper, we discuss the representation family of Horn logic. Work on tailoring representations is illustrated by a robot application.