摘要 :
In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data ...
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In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mining algorithms, as well as their composition into nontrivial multi-step knowledge discovery scenarios relevant for practical application. We proceed by laying out some basic concepts, starting with (structured) data and generalizations (e.g., patterns and models) and continuing with data mining tasks and basic components of data mining algorithms (I.e., refinement operators, distances, features and kernels). We next discuss how to use these concepts to formulate constraint-based data mining tasks and design generic data mining algorithms. We finally discuss how these components would fit in the overall framework and in particular into a language for data mining and knowledge discovery.
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摘要 :
In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data ...
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In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mining algorithms, as well as their composition into nontrivial multistep knowledge discovery scenarios relevant for practical application. We proceed by laying out some basic concepts, starting with (structured) data and generalizations (e.g., patterns and models) and continuing with data mining tasks and basic components of data mining algorithms (i.e., refinement operators, distances, features and kernels). We next discuss how to use these concepts to formulate constraint-based data mining tasks and design generic data mining algorithms. We finally discuss how these components would fit in the overall framework and in particular into a language for data mining and knowledge discovery.
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摘要 :
In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data ...
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In this paper, we address the ambitious task of formulating a general framework for data mining. We discuss the requirements that such a framework should fulfill: It should elegantly handle different types of data, different data mining tasks, and different types of patterns/models. We also discuss data mining languages and what they should support: this includes the design and implementation of data mining algorithms, as well as their composition into nontrivial multi-step knowledge discovery scenarios relevant for practical application. We proceed by laying out some basic concepts, starting with (structured) data and generalizations (e.g., patterns and models) and continuing with data mining tasks and basic components of data mining algorithms (I.e., refinement operators, distances, features and kernels). We next discuss how to use these concepts to formulate constraint-based data mining tasks and design generic data mining algorithms. We finally discuss how these components would fit in the overall framework and in particular into a language for data mining and knowledge discovery.
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This chapter introduces Inductive Logic Programming (ILP) and Learning Language in Logic (LLL). No previous knowledge of logic programming, ILP or LLL is assumed. Elementary topics are covered and more advanced topics are discusse...
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This chapter introduces Inductive Logic Programming (ILP) and Learning Language in Logic (LLL). No previous knowledge of logic programming, ILP or LLL is assumed. Elementary topics are covered and more advanced topics are discussed. For example, in the ILP section we discuss subsumption, inverse resolution, least general generalisation, relative least general generalisation, inverse entailment, saturation, refinement and abduction. We conclude with an overview of this volume and pointers to future work.
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In this chapter, we focus on the equation discovery task, i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain kn...
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In this chapter, we focus on the equation discovery task, i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain knowledge in the process of equation discovery. The proposed methodology transforms the available domain knowledge to a grammar specifying the space of candidate equation-based models. We show here how various aspects of knowledge about modeling dynamic systems in a particular domain of interest can be transformed to grammars. Thereafter, the equation discovery method LAGRAMGE can search through the space of models specified by the grammar and find ones that fit measured data well. We illustrate the utility of the proposed methodology on three modeling tasks from the domain of Environmental sciences. All three tasks involve establishing models of real-world systems from noisy measurement data.
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摘要 :
In this chapter, we focus on the equation discovery task. i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain kn...
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In this chapter, we focus on the equation discovery task. i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain knowledge in the process of equation discovery. The proposed methodology transforms the available domain knowledge to a grammar specifying the space of candidate equation-based models. We show here how various aspects of knowledge about modeling dynamic systems in a particular domain of interest can be transformed to grammars. Thereafter, the equation discovery method LAGRAMGE can search through the space of models specified by the grammar and find ones that fit measured data well. We illustrate the utility of the proposed methodology on three modeling tasks from the domain of Environmental sciences. All three tasks involve establishing models of real-world systems from noisy measurement data.
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摘要 :
In this chapter, we focus on the equation discovery task. i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain kn...
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In this chapter, we focus on the equation discovery task. i.e., the task of inducing models based on algebraic and ordinary differential equations from measured and observed data. We propose a methodology for integrating domain knowledge in the process of equation discovery. The proposed methodology transforms the available domain knowledge to a grammar specifying the space of candidate equation-based models. We show here how various aspects of knowledge about modeling dynamic systems in a particular domain of interest can be transformed to grammars. Thereafter, the equation discovery method LAGRAMGE can search through the space of models specified by the grammar and find ones that fit measured data well. We illustrate the utility of the proposed methodology on three modeling tasks from the domain of Environmental sciences. All three tasks involve establishing models of real-world systems from noisy measurement data.
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Inductive databases (IDBs) contain both data and patterns. Inductive Queries (IQs) are used to access, generate and manipulate the patterns in the IDB. IQs are conjunctions of primitive constraints that have to be satisfied by tar...
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Inductive databases (IDBs) contain both data and patterns. Inductive Queries (IQs) are used to access, generate and manipulate the patterns in the IDB. IQs are conjunctions of primitive constraints that have to be satisfied by target patterns: they can be different for different types of patterns. Constraint-based data mining algorithms are used to answer IQs. So far, mostly the problem of mining frequent patterns has been considered in the framework of IDBs: the types of patterns considered include frequent itemsets, episodes, Datalog queries, sequences, and molecular fragments. Here we consider the problem of constraint-based mining for predictive models, where the data mining task is regression and the models are polynomial equations. More specifically, we first define the pattern domain of polynomial equations. We then present a complete and a heuristic solver for this domain. We evaluate the use of the heuristic solver on standard regression problems and illustrate its use on a toy problem of reconstructing a biochemical reaction network. Finally, we consider the use of a combination of different pattern domains (molecular fragments and polynomial equations) for practical applications in modeling quantitative structure-activity relationships (QSARs).
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摘要 :
Inductive databases (IDBs) contain both data and patterns. Inductive Queries (IQs) are used to access, generate and manipulate the patterns in the IDB. IQs are conjunctions of primitive constraints that have to be satisfied by tar...
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Inductive databases (IDBs) contain both data and patterns. Inductive Queries (IQs) are used to access, generate and manipulate the patterns in the IDB. IQs are conjunctions of primitive constraints that have to be satisfied by target patterns: they can be different for different types of patterns. Constraint-based data mining algorithms are used to answer IQs.
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This chapter is concerned with integrating knowledge-based modeling or modeling from first principles, with data-driven or automated modeling of dynamic systems. The approach presented here includes methods for equation discovery:...
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This chapter is concerned with integrating knowledge-based modeling or modeling from first principles, with data-driven or automated modeling of dynamic systems. The approach presented here includes methods for equation discovery: Unlike mainstream system identification methods, which work under the assumption that the form of the equations is known, equation discovery systems explore a space of possible equation structures. We propose a formalism for representing knowledge about processes in population dynamics domains and a method to transform such knowledge into an operational form that could be used by equation discovery systems. We also describe the extensions of the equation discovery system LAGRAMGE necessary to incorporate this kind of knowledge in the process of equation discovery.
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