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22nd Pacific-Asia conference on Advances in knowledge discovery and data mining, PAKDD 2018

作者： Wei, Jipeng Wei, Qianjin Wen, Yimin School of Computer Science and Information Safety Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9783319930398

Solving minimal attribute reduction (MAR) in rough set theory is a NP-hard and nonlinear constrained combinatorial optimization problem. Ant colony optimization (ACO), a new intelligent computing method, takes strategies of heuristic search, which is characterized by a distributed and positive feedback and it has the advantage of excellent global optimization ability for handling combinatorial optimization problems. Having considered that the existing information entropy and information gain methods fail to help to select the optimal minimal attribute every time, this paper proposed a novel attribute reduction algorithm based on ACO. Firstly, the algorithm adopts an improved information gain rate as heuristic information. Secondly, each ant solves a problem of minimum attributes reduction and then conduct redundancy test to each selected attribute. What’s more, redundant detection of all non-core attributes in the optimal solution will be perfomed in each generation. The result of the experiment on several datasets from UCI show that the proposed algorithms are more capable of finding the minimum attribute reduction and can faster converge and at the same time they can almost keep the classification accuracy, compared with the traditional attribute reduction based on ACO algorithm. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Rough set theory

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Dependency Networks Extractions from Textual Sources - A Case Study in Criminology

Dependency Networks Extractions from Textual Sources - A Cas...

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Intelligent Systems conference (IntelliSys)

作者： Trovati, Marcello Edge Hill Univ Dept Comp Sci Ormskirk England

ISBN: (纸本)9781509064359

The identification and assessment of data is one of the crucial challenges within any scientific discipline. Due to the size, complexity of data, and its internal contradictory information, there are several challenges, which need to be fully addressed to ensure the advance of data Science and its applications. This work focuses on an automatic approach to extract, identify and discover knowledge focusing on the creation on Dependency Networks (DN). These are powerful modelling tools to navigate throughout data and allow to determine how concepts influence one another. The main motivation of this work is to propose a method to facilitate the decision making and knowledge discovery process. The validation of the proposed approach will demonstrate the potential of this work, specifically focussing on Criminology.

关键词： Network theory text mining dependency networks criminology

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Galois's algebraic structure and bipartite graph spatio-structural analytics for urban public transportation system assessment 8

Galois's algebraic structure and bipartite graph spatio-stru...

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8th International conference on Ambient Systems, Networks and Technologies (ANT) / 7th International conference on Sustainable Energy Information technology (SEIT)

作者： Boulmakoul, Badredine Besri, Zineb Karim, Lamia Boulmakoul, Azedine Lbath, Ahmed Fac Sci & Technol Mohammaedia BP 146 Mohammadia 20650 Morocco Natl Sch Appl Sci Tetouan BP 2222 Mhannech Tetouan Morocco Hassan 1st Univ Higher Sch Technol EST Berrechid Khouribga Morocco Univ Grenoble Alpes CNRS LIG MRIM France Grenoble France

The analysis of complex systems remains the domain of excellence to grasp for control complexity. Theoretical foundations derived from order theory (Galois lattice, spectral analysis of discrete graphs) provide interesting solutions to study exchanges and flows in a system. These are systemic tools well suited to big linked data mining. Various applications exist in the areas of computer networks, social networks analysis, economic analysis and, knowledge discovery. This paper involves Galois Lattice operations and centrality based on spectral analysis for the discovery of the organization and the existing linkage between transport supply given by the routes served by transport (bus, tram, train) and demand resulting in spatial zones (the stop stations). The main objective of this work is to develop a structural knowledge and patterns discovery framework to discover deep knowledge ensuing from the structural representation of the public transport network. The case study concerns the analysis of urban transportation systems (tram and bus) of the city of Valenciennes (France). However, the approach remains valid for other networks without any alterations or modifications. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Galois lattice bipartite graph linked-data

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Advances in knowledge discovery and data mining: 21st Pacific-Asia conference, PAKDD 2017 Jeju, South Korea, may 23–26, 2017 proceedings, part ii 21st

Advances in knowledge discovery and data mining: 21st Pacifi...

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21st Pacific-Asia conference on knowledge discovery and data mining, PAKDD 2017

作者： Kim, Jinho Shim, Kyuseok Cao, Longbing Lee, Jae-Gil Lin, Xuemin Moon, Yang-Sae Kangwon National University Chuncheon Korea Republic of Seoul National University Seoul Korea Republic of University of Technology Sydney SydneyNSW Australia KAIST Daejeon Korea Republic of University of New South Wales SydneyNSW Australia

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A Collaborative Intrusion Detection Based on K-means and Projective Adaptive Resonance theory 13

A Collaborative Intrusion Detection Based on K-means and Pro...

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13th International conference on Natural Computation, Fuzzy Systems and knowledge discovery (ICNC-FSKD)

作者： Aung, Yi Yi Min, Myat Myat Univ Comp Studies Mandalay Mandalay Myanmar

ISBN: (纸本)9781538621653

Since the start of the 21st century, computer networks have been through an exponential growth in terms of the network capacity, the number of the users and the type of tasks that are performed over the network. With the improving advanced technology of portable devices such as smart phones, tablet, smart devices, other computing devices, the number of network users are increasing more and more. Hence, security on network is very important for all net consumers. Intrusion detection is the process of defending intrusions. The action of entering to a system without permission is called intrusion. An intrusion detection system (IDS) can detect all incoming and out coming intrusion to the system. IDS can inspect all possible violations of a security guideline by monitoring system activities. Since IDS are fundamental part of security boundary, it can afford the ability to identify security holes in a system. There are many current methods used in intrusion detection. This paper analyzes the comparison between hybrid data mining methods and single method. The purpose of the system is to show that using hybrid data mining methods can reduce time complexity of the system than single method. This model was verified using KDD'99 data set. Experimental results clearly show that hybrid methods using K-means and Projective Adaptive Resonance theory can significantly reduce model training time of the system and also it maintains the accuracy of detections.

关键词： Intrusion Detection System KDD CUP'99 dataset Kmeans Projective Adaptive Resonance theory

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Feature Selection based on Discernibility Function in Incomplete data with Fuzzy Decision 29

Feature Selection based on Discernibility Function in Incomp...

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29th Annual IEEE International conference on tools with Artificial Intelligence (ICTAI)

作者： Qian, Wenbin Shu, Wenhao Liu, Jun Wang, Yinglong Jiangxi Agr Univ Sch Software Nanchang 330045 Jiangxi Peoples R China Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China

ISBN: (纸本)9781538638767

Rough set theory has been applied successfully in knowledge discovery, computational intelligence and decision analysis. It can only deal with features of symbolic type for complete data. However, incomplete data with fuzzy decision under a preference-ordered relation is common in real-world applications. In this paper, we propose a feature selection framework for such data by combining the dominance-based rough sets. At first, the judgment theorems are established by the Boolean reasoning techniques. Then, the discernibility matrix and the discernibility function approach are proposed to find all subsets of features. In addition, an efficient feature selection algorithm to find a feature subset is proposed. Finally, the experimental results show that, in most cases for different data sets, the proposed algorithm is effective and efficient for feature selection from the incomplete data with fuzzy decision.

关键词： Feature selection Incomplete data Discernibility function knowledge discovery Rough sets

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MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data

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data mining AND knowledge discovery 2017年第5期31卷 1359-1390页

作者： Becker, Martin Lemmerich, Florian Singer, Philipp Strohmaier, Markus Hotho, Andreas Univ Wurzburg Data Min & Informat Retrieval Grp Wurzburg Germany GESIS Leibniz Inst Social Sci Cologne Germany

Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.

关键词： Heterogeneous sequence data Markov chain Model comparison Bayes factor Hyptrails Mixedtrails MTMC

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Deep associative semantic neural graphs for knowledge representation and fast data exploration 9

Deep associative semantic neural graphs for knowledge repres...

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9th International Joint conference on knowledge discovery, knowledge Engineering and knowledge Management, IC3K 2017

作者： Horzyk, Adrian Department of Automatics and Biomedical Engineering AGH University of Science and Technology in Krakow Mickiewicza Av. 30 Krakow30-059 Poland

ISBN: (纸本)9789897582721

This paper presents new deep associative neural networks that can semantically associate any data, represent their complex relations of various kinds, and be used for fast information search, data mining, and knowledge exploration. They allow to store various horizontal and vertical relations between data and significantly broaden and accelerate various search operations. Many relations which must be searched in the relational databases are immediately available using the presented associative data model based on a new special kind of associative spiking neurons and sensors used for the construction of these networks. The inference operations are also performed using the reactive abilities of these spiking neurons. The paper describes the transformation of any relational database to this kind of networks. All related data and their combinations representing various objects are contextually connected with different strengths reproducing various similarities, proximities, successions, orders, inclusions, rarities, or frequencies of these data. The computational complexity of the described operations is usually constant and less than operations used in the databases. The theory is illustrated by a few examples and used for inference on this kind of neural networks. © 2017 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved.

关键词： Semantics

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data Science for Financial Applications 18

Data Science for Financial Applications

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Proceedings of the 24th ACM SIGKDD International conference on knowledge discovery & data mining

作者： David J. Hand Imperial College London United Kingdom

ISBN: (纸本)9781450355520

Financial applications of data science provide a perfect illustration of the power of the shift from subjective decision-making to data- and evidence-driven decision-making. In the space of some fifty years, an entire sector of industry has been totally revolutionised. Such applications come in three broad areas: actuarial and insurance, consumer banking, and investment banking. Actuarial and insurance work was one of the earliest adopters of data science ideas, dating from long before the term had been coined, and even before the computer had been invented. But these areas have fallen behind the latest advances in data science technology - which means there is considerable potential for applying modern data analytic ideas. Consumer banking has been described as one the first and major success stories of the data revolution. Dating from the 1960s, when the first credit cards were launched, techniques for analysing the massive data sets of consumer financial transactions have driven much of the development of data mining and data science ideas. But new model types, and new sources of data, are leading to a rich opportunity for significant developments. In investment banking the "efficient market hypothesis" of classic economics says that it is impossible to predict the financial markets. But this is false - though very nearly true. That means that there is an opportunity to use advanced data analytic methods to exploit the tiny gap between conventional theory and what actually happens. Other data science issues, such as data quality, ethics, and security, along with the need to understand the limitations of models, become particularly pointed in the context of financial applications.

关键词： banking data science credit cards retail banking fraud detection

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Subjectively Interesting Connecting Trees

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European conference on Machine Learning and Principles and Practice of knowledge discovery in databases (ECML PKDD)

作者： Adriaens, Florian Lijffijt, Jefrey De Bie, Tijl Univ Ghent Imec Dept Elect & Informat Syst IDLab Ghent Belgium

ISBN: (纸本)9783319712468;9783319712451

Consider a large network, and a user-provided set of query nodes between which the user wishes to explore relations. For example, a researcher may want to connect research papers in a citation network, an analyst may wish to connect organized crime suspects in a communication network, or an internet user may want to organize their bookmarks given their location in the world wide web. A natural way to show how query nodes are related is in the form of a tree in the network that connects them. However, in sufficiently dense networks, most such trees will be large or somehow trivial (e.g. involving high degree nodes) and thus not insightful. In this paper, we define and investigate the new problem of mining subjectively interesting trees connecting a set of query nodes in a network, i.e., trees that are highly surprising to the specific user at hand. Using information theoretic principles, we formalize the notion of interestingness of such trees mathematically, taking in account any prior beliefs the user has specified about the network. We then propose heuristic algorithms to find the best trees efficiently, given a specified prior belief model. Modeling the user's prior belief state is however not necessarily computationally tractable. Yet, we show how a highly generic class of prior beliefs, namely about individual node degrees in combination with the density of particular sub-networks, can be dealt with in a tractable manner. Such types of beliefs can be used to model knowledge of a partial or total order of the network nodes, e.g. where the nodes represent events in time (such as papers in a citation network). An empirical validation of our methods on a large real network evaluates the different heuristics and validates the interestingness of the given trees.

关键词： Exploratory data mining Subjective interestingness Information theory Graphs Graph pattern mining

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