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Double threshold construction method for attribute-induced three-way concept lattice in incomplete fuzzy formal context

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JOURNAL OF ENGINEERING-JOE 2020年第13期2020卷 549-554页

作者： Long, Binghan Xu, Weihua Zhang, Xiaoyan Chongqing Univ Technol Sch Sci Chongqing 400054 Peoples R China Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China

Concept lattice theory is the basis of formal concept analysis. It is an effective tool for knowledge representation and discovery. Three-way decision theory is a new decision theory based on rough sets and decision rough sets. In this study, a method is proposed for constructing attribute-induced three-way concept lattices in an incomplete fuzzy formal context. First, the authors introduce the related knowledge of the classical formal context, the fuzzy formal context and incomplete fuzzy formal context in detail. They put forward a method to fill the incomplete formal context. Secondly, they propose a double threshold operation, which divides the relationship between objects and attributes into three parts: belonging, not belonging and undetermined. Thirdly, they propose three methods to complement incomplete formal context and introduce their advantages and disadvantages. Fourthly, they deal with incomplete formal context by special symbol completion method and get related algorithms of attribute-induced three-way concept lattice. Finally, a case is given to verify the effectiveness of the algorithm for constructing attribute-induced three-way concept lattices.

关键词： data mining formal concept analysis rough set theory fuzzy set theory knowledge representation lattice theory decision theory double threshold construction method concept lattice incomplete fuzzy formal context lattice theory formal concept analysis decision theory decision rough sets constructing attribute-induced three-way classical formal context incomplete formal context

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Forecasting Big Time Series: theory and Practice 19

Forecasting Big Time Series: Theory and Practice

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25th ACM SIGKDD International conference on knowledge discovery & data mining (KDD)

作者： Faloutsos, Christos Flunkert, Valentin Gasthaus, Jan Januschowski, Tim Wang, Yuyang Carnegie Mellon Univ Pittsburgh PA 15213 USA AWS AI Labs Palo Alto CA USA

ISBN: (纸本)9781450362016

Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions;in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning;and workforce scheduling in warehouses and factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model-and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources and result in a set of challenges that need to be addressed such as the following. How can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of "similar" time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes? The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in three related fields: (1) classical modeling of time series, (2) modern methods including tensor analysis and deep learning for forecasting. Furthermore, we discuss the practical aspects of building a large scale forecasting system, including data integration, feature generation, backtest framework, error tracking and analysis, etc. While our focus is on providing an intuitive overview of the methods and practical issues which we will illustrate via case studies and interactive materials with Jupyter notebooks.

关键词： Forecasting Tensor Analysis Neural Network Time Series

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Image representation for image mining: A study focusing on mining satellite images for census data collection 8th

Image representation for image mining: A study focusing on m...

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8th International Joint conference on knowledge discovery, knowledge Engineering and knowledge Management, IC3K 2016

作者： Coenen, Frans Dittakan, Kwankamon Department of Computer Science The University of Liverpool Liverpool L69 3BX United Kingdom Phuket Thailand

ISBN: (纸本)9783319997001

This paper firstly presents a taxonomy for mage representation in the context of image mining. The main premise being that the actual mining algorithms that may be used are well understood, it is the preprocessing of the image data that remains a challenge. The requirement for the output from this preprocessing is some image representation that us both sufficiently expressive while at the same time being compatible with the mining process to be applied. Three categories of representation are considered: (i) statistics-based, (ii) tree-based and (iii) point series based. The second contribution of this paper is an analysis of the proposed representations categories with respect to a novel image mining application, the collection of individual household census data from satellite imagery, more specifically Google earth satellite imagery. The representations are considered both in terms of generating census prediction models and in terms of applying such models for larger scale census prediction. © Springer Nature Switzerland AG 2019.

关键词： Satellite imagery

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Ranking decision rules using rough set theory—a comparative analysis

Ranking decision rules using rough set theory—a comparative...

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International conference on Cognitive Informatics and Soft Computing, CISC 2017

作者： Sabu, M.K. Department of Computer Applications Cochin University of Science and Technology CochinKerala India

ISBN: (纸本)9789811306167

Rule induction is one of the fundamental tasks in knowledge discovery system. The aim of rule mining is to discover hidden and interesting patterns from data contained in large datasets. Rough Set theory provides efficient mathematical models suitable for decision rule induction especially in the case of inconsistent datasets. To develop measures for extracting significant rules automatically from a large number of rules generated by a data mining system is a challenging problem in rule generation. In this paper, decision rules produced by a conventional rule mining system are ranked based on a rule evaluation measure called Degree of Rule Significance. Based on this measure a rule ranking algorithm is designed to rank the generated decision rules. The ranking given by the algorithm is evaluated with the help of two rough set based rule evaluation approaches. Experimental results show the effectiveness of Degree of Rule Significance to identify important rules. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Rough set theory

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CTI-Twitter: Gathering Cyber Threat Intelligence from Twitter using Integrated Supervised and Unsupervised Learning

CTI-Twitter: Gathering Cyber Threat Intelligence from Twitte...

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IEEE International conference on Big data

作者： Linn-Mari Kristiansen Vinti Agarwal Katrin Franke Raj Sanjay Shah Norwegian University of Science and Technology Gjovik Norway Birla Institute of Science and Technology Pilani India

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Cyber threat intelligence (CTI) can be gathered from multiple sources, and Twitter is one such open source platform where a large volume and variety of threat data is shared every day. The automated and timely mining of relevant threat knowledge from this data can be crucial for enrichment of existing threat intelligence platforms to proactively defend against cyber attacks. We propose CTI-Twitter: a novel frame-work combining supervised and unsupervised learning models to collect, process, analyze and generate threat specific knowledge from tweets coming from multiple users. CTI-Twitter has multi-fold contributions: i) first collecting tweets through Twitter API, ii) extracting relevant threat tweets from irrelevant ones, and classifying relevant ones into multiple classes of threats iii) then grouping tweets belonging to each class using topic modeling iv) finally performing data enrichment and verification process. We evaluate our proposed model on real-time tweets collected for about four months (in year 2020) using Twitter API. The encouraging results obtained indicate the effectiveness of CTI-Twitter in terms of timeliness and discovery of trending attacks patterns, and vulnerabilities.

关键词： Social networking (online) Blogs Big data Real-time systems data models data mining Unsupervised learning

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Heterogeneous Item Recommendation for the Air Travel Industry 23rd

Heterogeneous Item Recommendation for the Air Travel Industr...

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23rd Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： He, Zhicheng Liu, Jie Xu, Guanghui Huang, Yalou Nankai Univ Coll Comp Sci Tianjin Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin Peoples R China Nankai Univ Coll Software Tianjin Peoples R China

ISBN: (纸本)9783030161453;9783030161446

Analyzing the travel behaviors and patterns of air passengers have always been of great significance to the air travel industry. Understanding the demands and interests of passengers behind their behaviors is a crucial and fundamental task for many applications. However, this task is challenging due to the lack of customer information, data sparsity, and the long-tail distribution. In this paper, we investigate the problem of heterogeneous item recommendation by learning representations of items and passengers in a shared latent space. Specifically, we first establish a heterogeneous information network (HIN) through statistical analysis, where the edges represent the interactions between different nodes. Each node also contains some auxiliary attribute information that describes its travel behavior or that of its passenger groups. Then we devise a joint matrix factorization model to learn node representations based on the HIN, where both the heterogeneous edges and the node attributes are incorporated into the learning process. Moreover, a weighting strategy is further utilized to deal with the long-tail distribution of passenger behaviors based on the implicit feedback information. Experimental results conducted on a real-world passenger name record (PNR) dataset demonstrate the effectiveness of the proposed method.

关键词： Air travel data analysis Matrix factorization Air route recommendation Airline recommendation

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TPLP: Two-Phase Selection Link Prediction for Vertex in Graph Streams 23rd

TPLP: Two-Phase Selection Link Prediction for Vertex in Grap...

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23rd Pacific-Asia conference on knowledge discovery and data mining (PAKDD)

作者： Xiao, Yang Huang, Hong Zhao, Feng Jin, Hai Huazhong Univ Sci & Technol Natl Engn Ctr Big Data Technol & Syst Cluster & Grid Comp Lab Serv Comp Technol & Syst LabSch Comp Sci & Techn Wuhan 430074 Peoples R China

ISBN: (纸本)9783030161453;9783030161446

Currently, data in many applications have naturally been modeled as streams over the massive graph infrastructure, e.g., social networks and electronic business. Graph streams are rapidly changing, enormous and endless networks that are too large to maintain in memory or on disks. An important problem in networks is link prediction, which aims to estimate the likelihood of the existence of a specific link However, in graph streams, predicting the existence of links connected to one vertex is more common For example, in social networks, we generally want to recommend several friends to a user rather than determining whether a specific user is your friend. Rapidly and accurately predicting groups of links becomes a formidable challenge because of the tremendous size and rapidly updated information of graph streams. In this paper, we propose the problem of link prediction for vertex in graph streams, which aims to predict the top-k vertices, i.e., the top-k links, that are most likely to connect to the target vertex in graph streams. A two-phase selection framework is proposed to predict top-k links with high efficiency and without loss of accuracy. We also propose a novel method for estimating common neighbor in graph streams, which is a very important measure in link prediction. Extensive experiments show that our algorithms are more efficient and more accurate than state-of-the-art methods.

关键词： Forecasting

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MotiMul: A significant discriminative sequence motif discovery algorithm with multiple testing correction

MotiMul: A significant discriminative sequence motif discove...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Koichi Mori Haruka Ozaki Tsukasa Fukunaga Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan Faculty of Medicine University of Tsukuba Tokyo Japan

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Sequence motifs play essential roles in intermolecular interactions such as DNA-protein interactions. The discovery of novel sequence motifs is therefore crucial for revealing gene functions. Various bioinformatics tools have been developed for finding sequence motifs, but until now there has been no software based on statistical hypothesis testing with statistically sound multiple testing correction. Existing software therefore could not control for the type-l error rates. This is because, in the sequence motif discovery problem, conventional multiple testing correction methods produce very low statistical power due to overly-strict correction. We developed MotiMul, which comprehensively finds significant sequence motifs using statistically sound multiple testing correction. Our key idea is the application of Tarone's correction, which improves the statistical power of the hypothesis test by ignoring hypotheses that never become statistically significant. For the efficient enumeration of the significant sequence motifs, we integrated a variant of the PrefixSpan algorithm with Tarone's correction. Simulation and empirical dataset analysis showed that MotiMul is a powerful method for finding biologically meaningful sequence motifs. The source code of MotiMul is freely available at https://***/ko-ichimo-ri/MotiMul.

关键词： Testing Bioinformatics data mining tools Genomics Software Biological information theory

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A Memetic Algorithm Approach to Network Alignment Mapping the Classification of Mental Disorders of DSM-IV with ICD-10

A Memetic Algorithm Approach to Network Alignment Mapping th...

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Genetic and Evolutionary Computation conference (GECCO)

作者： Haque, Mohammad Nazmul Mathieson, Luke Moscato, Pablo Univ Newcastle Sch Elect Engn & Comp Callaghan NSW Australia Univ Technol Sydney Sch Software Ultimo NSW Australia

ISBN: (纸本)9781450361118

Given two graphs modelling related, but possibly distinct, networks, the alignment of the networks can help identify significant structures and substructures which may relate to the functional purpose of the network components. The Network Alignment Problem is the NP-hard computational formalisation of this goal and is a useful technique in a variety of data mining and knowledge discovery domains. In this paper we develop a memetic algorithm to solve the Network Alignment Problem and demonstrate the effectiveness of the approach on a series of biological networks against the existing state of the art alignment tools. We also demonstrate the use of network alignment as a clustering and classification tool on two mental health disorder diagnostic databases.

关键词： Network Alignment Memetic Algorithm Graph theory DSM-IV ICD-10 protein-protein Interaction network psychopathology

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Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported data 19

Sequence Multi-task Learning to Forecast Mental Wellbeing fr...

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25th ACM SIGKDD International conference on knowledge discovery & data mining (KDD)

作者： Spathis, Dimitris Servia-Rodriguez, Sandra Farrahi, Katayoun Mascolo, Cecilia Rentfrow, Jason Univ Cambridge Dept Comp Sci & Tech Cambridge England Univ Southampton Dept Elect & Comp Sci Southampton Hants England Univ Cambridge Dept Psychol Cambridge England

ISBN: (纸本)9781450362016

Smartphones have started to be used as self reporting tools for mental health state as they accompany individuals during their days and can therefore gather temporally. ne grained data. However, the analysis of self reported mood data offers challenges related to non-homogeneity of mood assessment among individuals due to the complexity of the feeling and the reporting scales, as well as the noise and sparseness of the reports when collected in the wild. In this paper, we propose a new end-to-end ML model inspired by video frame prediction and machine translation, that forecasts future sequences of mood from previous self-reported moods collected in the real world using mobile devices. Contrary to traditional time series forecasting algorithms, our multi-task encoder-decoder recurrent neural network learns patterns from different users, allowing and improving the prediction for users with limited number of self-reports. Unlike traditional feature-based machine learning algorithms, the encoder-decoder architecture enables to forecast a sequence of future moods rather than one single step. Meanwhile, multi-task learning exploits some unique characteristics of the data (mood is bi-dimensional), achieving better results than when training single-task networks or other classifiers. Our experiments using a real-world dataset of 33, 000 user-weeks revealed that (i) 3 weeks of sparsely reported mood is the optimal number to accurately forecast mood, (ii) multi-task learning models both dimensions of mood - valence and arousal-with higher accuracy than separate or traditional ML models, and (iii) mood variability, personality traits and day of the week play a key role in the performance of our model. We believe this work provides psychologists and developers of future mobile mental health applications with a ready-to-use and effective tool for early diagnosis of mental health issues at scale.

关键词： multi-task learning sequence learning recurrent neural networks mood forecasting

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