In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of ...
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ISBN:
(纸本)9783642111631
In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store.
This paper presents a method of mining the data obtained by a collection of pressure sensors monitoring a pipe network to obtain information about the location and size of leaks in the network This inverse engineering...
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ISBN:
(纸本)9781424450879
This paper presents a method of mining the data obtained by a collection of pressure sensors monitoring a pipe network to obtain information about the location and size of leaks in the network This inverse engineering problem is effected by support vector machines (SVMs) which act as pattern recognisers. In this study the SVMs are trained and tested on data obtained from the EPANET hydraulic modelling system. Performance assessment of the SVM showed that leak size and location are both predicted with a reasonable degree of accuracy. The information obtained from this SVM analysis would be invaluable to water authorities in overcoming the ongoing problem of leak detection.
作者：
He, JingWuhan Univ
Econ & Management Sch Wuhan 430072 Peoples R China
The datamining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machinelearning, pattern recognit...
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ISBN:
(纸本)9780769538594
The datamining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machinelearning, patternrecognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. datamining represents a set of specific algorithms of finding useful meanings in stored data. This paper aims to point the most important steps that were made in the datamining field of study in recent years and to show how the overall process of discovering can be improved in the future.
In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity mea...
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ISBN:
(纸本)9783642111631
In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.
This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and patternrecognition, relies also o...
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ISBN:
(纸本)9783642030697
This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and patternrecognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic, inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis, tasks showed improvement performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kern...
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ISBN:
(纸本)9783642030697
Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.
This paper proposes a navigational method for mining by collecting evidences from diverse data sources. Since the representation method and even semantics of data elements differ widely from one data source to the oth...
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ISBN:
(纸本)9783642111631
This paper proposes a navigational method for mining by collecting evidences from diverse data sources. Since the representation method and even semantics of data elements differ widely from one data source to the other, consolidation of data under a single platform doesn't become cost effective. Instead, this paper has proposed a method of mining in steps where knowledge gathered in one step or from one data source is transferred to the next step or next data source exploiting a distributed environment. This incremental mining process ultimately helps in arriving at the desired result. The entire work has been done in the domain of systems biology. Indication has been given how this process can be followed in other application areas as well.
No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we, in...
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ISBN:
(纸本)9783642030697
No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we, investigate what happens when a set of no-regret learners (voters) merge their respective decisions in each learning iteration to a single, common one in form of a convex combination. We show that an agent (or algorithm) that executes this merged decision in each iteration of the online learning process and each time feeds back a copy of its own reward function to the voters, incurs sublinear regret itself. As a by-product, we obtain a simple method that allows us to construct new no-regret algorithms out of known ones.
Prior knowledge about it problem domain can be utilized to bias Support Vector machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improve...
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ISBN:
(纸本)9783642030697
Prior knowledge about it problem domain can be utilized to bias Support Vector machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improved generalization performance after the application of domain knowledge;especially in the case of scarce training data. In this paper, we propose an extension to the Virtual Support vectors (VSVs) technique where only a subset of the Support vectors (SVs) is Utilized. Unlike previous methods, the Purpose here is to compensate for noise and uncertainty in the training data. Furthermore, we investigate the effect of domain knowledge not only oil the quality of the SVM model, but also Oil rules extracted from it: hence the learned pattern by the SVM. Results on five benchmark and one real life data sets show that domain knowledge can significantly improve both the quality Of the SVM and the rules extracted from it.
Manufacturing process data collected over time are considered time-series data and can be arranged into control charts. Important applications can be centered around these data like, for example, recognition of specif...
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ISBN:
(纸本)9781424441358
Manufacturing process data collected over time are considered time-series data and can be arranged into control charts. Important applications can be centered around these data like, for example, recognition of specific patterns, pattern similarity, detecting anomalies, and clustering and classification of patterns. We study and evaluate a number of classification techniques for process control data. For pattern similarity, we examine distance measure with raw data and with new feature extracted from the data. The evaluation is conducted with common benchmark process control data for time series process variables. This paper shows that datamining and machinelearning can be extremely beneficial in acquiring and producing knowledge and discoveries form process data to benefit the industry.
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