528 research outputs found

    Data exploration systems for databases

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    Data exploration systems apply machine learning techniques, multivariate statistical methods, information theory, and database theory to databases to identify significant relationships among the data and summarize information. The result of applying data exploration systems should be a better understanding of the structure of the data and a perspective of the data enabling an analyst to form hypotheses for interpreting the data. This paper argues that data exploration systems need a minimum amount of domain knowledge to guide both the statistical strategy and the interpretation of the resulting patterns discovered by these systems

    K-Means+ID3 and dependence tree methods for supervised anomaly detection

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    In this dissertation, we present two novel methods for supervised anomaly detection. The first method K-Means+ID3 performs supervised anomaly detection by partitioning the training data instances into k clusters using Euclidean distance similarity. Then, on each cluster representing a density region of normal or anomaly instances, an ID3 decision tree is built. The ID3 decision tree on each cluster refines the decision boundaries by learning the subgroups within a cluster. To obtain a final decision on detection, the k-Means and ID3 decision trees are combined using two rules: (1) the nearest neighbor rule; and (2) the nearest consensus rule. The performance of the K-Means+ID3 is demonstrated over three data sets: (1) network anomaly data, (2) Duffing equation data, and (3) mechanical system data, which contain measurements drawn from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical mass beam system subjected to fatigue stress, respectively. Results show that the detection accuracy of the K-Means+ID3 method is as high as 96.24 percent on network anomaly data; the total accuracy is as high as 80.01 percent on mechanical system data; and 79.9 percent on Duffing equation data. Further, the performance of K-Means+ID3 is compared with individual k-Means and ID3 methods implemented for anomaly detection. The second method dependence tree based anomaly detection performs supervised anomaly detection using the Bayes classification rule. The class conditional probability densities in the Bayes classification rule are approximated by dependence trees, which represent second-order product approximations of probability densities. We derive the theoretical relationship between dependence tree classification error and Bayes error rate and show that the dependence tree approximation minimizes an upper bound on the Bayes error rate. To improve the classification performance of dependence tree based anomaly detection, we use supervised and unsupervised Maximum Relevance Minimum Redundancy (MRMR) feature selection method to select a set of features that optimally characterize class information. We derive the theoretical relationship between the Bayes error rate and the MRMR feature selection criterion and show that MRMR feature selection criterion minimizes an upper bound on the Bayes error rate. The performance of the dependence tree based anomaly detection method is demonstrated on the benchmark KDD Cup 1999 intrusion detection data set. Results show that the detection accuracies of the dependence tree based anomaly detection method are as high as 99.76 percent in detecting normal traffic, 93.88 percent in detecting denial-of-service attacks, 94.88 percent in detecting probing attacks, 86.40 percent in detecting user-to-root attacks, and 24.44 percent in detecting remote-to-login attacks. Further, the performance of dependence tree based anomaly detection method is compared with the performance of naïve Bayes and ID3 decision tree methods as well as with the performance of two anomaly detection methods reported in recent literature

    Application of decision trees and multivariate regression trees in design and optimization

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    Induction of decision trees and regression trees is a powerful technique not only for performing ordinary classification and regression analysis but also for discovering the often complex knowledge which describes the input-output behavior of a learning system in qualitative forms;In the area of classification (discrimination analysis), a new technique called IDea is presented for performing incremental learning with decision trees. It is demonstrated that IDea\u27s incremental learning can greatly reduce the spatial complexity of a given set of training examples. Furthermore, it is shown that this reduction in complexity can also be used as an effective tool for improving the learning efficiency of other types of inductive learners such as standard backpropagation neural networks;In the area of regression analysis, a new methodology for performing multiobjective optimization has been developed. Specifically, we demonstrate that muitiple-objective optimization through induction of multivariate regression trees is a powerful alternative to the conventional vector optimization techniques. Furthermore, in an attempt to investigate the effect of various types of splitting rules on the overall performance of the optimizing system, we present a tree partitioning algorithm which utilizes a number of techniques derived from diverse fields of statistics and fuzzy logic. These include: two multivariate statistical approaches based on dispersion matrices, an information-theoretic measure of covariance complexity which is typically used for obtaining multivariate linear models, two newly-formulated fuzzy splitting rules based on Pearson\u27s parametric and Kendall\u27s nonparametric measures of association, Bellman and Zadeh\u27s fuzzy decision-maximizing approach within an inductive framework, and finally, the multidimensional extension of a widely-used fuzzy entropy measure. The advantages of this new approach to optimization are highlighted by presenting three examples which respectively deal with design of a three-bar truss, a beam, and an electric discharge machining (EDM) process

    Multi-dimensional clustering in user profiling

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    User profiling has attracted an enormous number of technological methods and applications. With the increasing amount of products and services, user profiling has created opportunities to catch the attention of the user as well as achieving high user satisfaction. To provide the user what she/he wants, when and how, depends largely on understanding them. The user profile is the representation of the user and holds the information about the user. These profiles are the outcome of the user profiling. Personalization is the adaptation of the services to meet the user’s needs and expectations. Therefore, the knowledge about the user leads to a personalized user experience. In user profiling applications the major challenge is to build and handle user profiles. In the literature there are two main user profiling methods, collaborative and the content-based. Apart from these traditional profiling methods, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, the profiling, achieved through these works, is lacking in terms of accuracy. This is because, all information within the profile has the same influence during the profiling even though some are irrelevant user information. In this thesis, a primary aim is to provide an insight into the concept of user profiling. For this purpose a comprehensive background study of the literature was conducted and summarized in this thesis. Furthermore, existing user profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these algorithms for user profiling was examined. A number of classification and clustering algorithms, such as Bayesian Networks (BN) and Decision Trees (DTs) have been simulated using user profiles and their classification accuracy performances were evaluated. Additionally, a novel clustering algorithm for the user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed. The MDC is a modified version of the Instance Based Learner (IBL) algorithm. In IBL every feature has an equal effect on the classification regardless of their relevance. MDC differs from the IBL by assigning weights to feature values to distinguish the effect of the features on clustering. Existing feature weighing methods, for instance Cross Category Feature (CCF), has also been investigated. In this thesis, three feature value weighting methods have been proposed for the MDC. These methods are; MDC weight method by Cross Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC) and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of these weighted MDC algorithms have been tested and evaluated. Additional simulations were carried out with existing weighted and non-weighted IBL algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user profiling to improve personalized service provisioning in mobile environments. The experiments presented in this thesis were conducted by using user profile datasets that reflect the user’s personal information, preferences and interests. The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA (version 3.5.7) machine learning platform. WEKA serves as a workbench to work with a collection of popular learning schemes implemented in JAVA. In addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life scenario is implemented as a Java Mobile Application (Java ME) on NetBeans IDE 7.1. All simulation results were evaluated based on the error rate and accuracy

    Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

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    Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

    An overview of decision table literature 1982-1995.

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    This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

    Machine Learning Algorithms and Fuzzy Neural Networks: an Experimental Comparison

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