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Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and... more
Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and technologies. AIAI focuses on real world applications; therefore authors should highlight the benefits of AI technology for industry and services. Novel approaches solving business and industrial problems, using AI, will emerge from this conference.
After reading this chapter you should be able to: use arrays when writing PHP scripts, especially in conjunction with the Foreach statement use the various sort functions available in PHP and understand the differences between them... more
After reading this chapter you should be able to: use arrays when writing PHP scripts, especially in conjunction with the Foreach statement use the various sort functions available in PHP and understand the differences between them distinguish between indexed arrays and associative arrays use the explode and implode functions to convert strings with internal separator characters, such as commas, into arrays and vice versa.
This chapter introduces the FP-growth algorithm for extracting frequent itemsets from a database of transactions. First the database is processed to produce a data structure called a FP-tree, then the tree is processed recursively by... more
This chapter introduces the FP-growth algorithm for extracting frequent itemsets from a database of transactions. First the database is processed to produce a data structure called a FP-tree, then the tree is processed recursively by constructing a sequence of reduced trees known as conditional FP-trees, from which the frequent itemsets are extracted. The algorithm has the very desirable feature of requiring only two scans through the database.
Hypertext/text domains are characterized by several tens or hundreds of thousands of features. This represents a challenge for supervised learning algorithms which have to learn accurate classifiers using a small set of available training... more
Hypertext/text domains are characterized by several tens or hundreds of thousands of features. This represents a challenge for supervised learning algorithms which have to learn accurate classifiers using a small set of available training examples. In this paper, a fuzzy semi-supervised support vector machines (FSS-SVM) algorithm is proposed. It tries to overcome the need for a large labelled training set. For this, it uses both labelled and unlabelled data for training. It also modulates the effect of the unlabelled data in the learning process. Empirical evaluations with two real-world hypertext datasets showed that, by additionally using unlabelled data, FSS-SVM requires less labelled training data than its supervised version, support vector machines, to achieve the same level of classification performance. Also, the incorporated fuzzy membership values of the unlabelled training patterns in the learning process have positively influenced the classification performance in compari...
The concern here is with the solution of a specialized form of problem (closely concerned with the game of chess) by means of heuristic methods. Defined is an expert system, RETRO, whose domain of application is retrograde-analysis chess... more
The concern here is with the solution of a specialized form of problem (closely concerned with the game of chess) by means of heuristic methods. Defined is an expert system, RETRO, whose domain of application is retrograde-analysis chess problems. This type of problem, chess logic problems as they are sometimes called, differs from the conventional type of chess problem in that it is concerned only with the past history of the game, and what may be deduced about it. Typically, a (human) solution proceeds by the solver asking himself a series of questions in the form of a Socratic dialogue until a solution emerges. RETRO makes use of a frame-like approach to determine the questions that must be asked to effect a solution. Although RETRO cannot solve any conceivable retrograde-analysis problem, the approach taken has been designed to be of general applicability.
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Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones,... more
Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for ...
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A brief overview of the history of the development of decision tree induction algorithms is followed by a review of techniques for dealing with missing attribute values in the operation of these methods. The technique of dynamic path... more
A brief overview of the history of the development of decision tree induction algorithms is followed by a review of techniques for dealing with missing attribute values in the operation of these methods. The technique of dynamic path generation is described in the context of tree-based classification methods. The waste of data which can result from casewise deletion of missing
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Abstract-This paper describes the facilities available in Inducer, a public domain rule induction workbench aimed at users who may not be computer scientists, who wish to analyze their own datasets using a range of data mining strategies... more
Abstract-This paper describes the facilities available in Inducer, a public domain rule induction workbench aimed at users who may not be computer scientists, who wish to analyze their own datasets using a range of data mining strategies or to conduct experiments with a given ...
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... Artificial Intelligence and Knowledge Management 11 Hugo Cesar Hoeschl and Vania Bar cellos ... Basis Functions Versus Geostatistics in Spatial 119 Interpolations Cristian Rusu, Virginia ... Multitree-Multiobjective Multicast Routing... more
... Artificial Intelligence and Knowledge Management 11 Hugo Cesar Hoeschl and Vania Bar cellos ... Basis Functions Versus Geostatistics in Spatial 119 Interpolations Cristian Rusu, Virginia ... Multitree-Multiobjective Multicast Routing for Traffic 247 Engineering Joel Prieto, Benjamin ...
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This paper describes the novel Knowledge Discovery system CUPID. Knowledge Discovery from Databases (KDD) is concerned with utilising techniques borrowed from fields such as machine learning (ML), statistics and databases to search for... more
This paper describes the novel Knowledge Discovery system CUPID. Knowledge Discovery from Databases (KDD) is concerned with utilising techniques borrowed from fields such as machine learning (ML), statistics and databases to search for relationships and global patterns that may exist in large databases, but arehidden' among the vast amounts of data. The discovered knowledge can be helpful for building knowledge based systems and data analysis. The underlying principle behind CUPID is the use of a quantitative measure for theinterest' of a hypotheses. This measure provides a method of ranking competing hypotheses and thus allows the system to store the 'best' or 'most interesting' rules describing a database. CUPID is based on the ITRule algorithm of (Smyth & Goodman, 1992) and extends that algorithm with added functionality. CUPID provides four fundamental features. One, background knowledge in the form of attribute value generalisation hierarchies may be utilised. Two, prior domain knowledge which may be incorrect and incomplete may be provided by a domain expert. Three, knowledge may be re-used. Four, noise in the data set is handled in a well founded manner.
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Abstract. The automatic induction of classification rules from examples in the form of a classification tree is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. In... more
Abstract. The automatic induction of classification rules from examples in the form of a classification tree is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. In some cases this can lead to an excessively large number of ...