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Knowledge Discovery In GIS Data [article]

Ayman Taha
2016 arXiv   pre-print
A new model to detect spatial outliers is also presented based on the new definition of the spatial neighborhood relationship. This model is adapted to be applied to polygonal objects.  ...  The spatial outlier detection is one of the most popular spatial data mining techniques which is used to detect spatial objects whose non-spatial attributes values are extremely different from those of  ...  We find the average percentage of improvement which is made by the proposed model with respect to classical spatial outlier model in this application is about 8%.  ... 
arXiv:1601.07241v1 fatcat:ljjln7jzcneodflc5v7x5ddd7m

A Framework for Outlier Detection in Geographic Spatial Data

Nita M.Dimble, Bharat Tidke
2015 International Journal in Foundations of Computer Science & Technology  
Because of sparse data clustering algorithm which are based on distance will not work to find outliers in spatial data. Problem of finding irregular feature in spatial data need to be explore.  ...  Many existing approaches have been proposed to overcome the problem of outlier detection in spatial Geographic data.  ...  A new framework has been proposed to find outlier using LOF algorithm. The experimental results shows proposed framework works efficiently in real world spatial data.  ... 
doi:10.5121/ijfcst.2015.5206 fatcat:ukmqksrzmvezrkhsgex2kmy44e

Spatial data mining for finding nearest neighbor and outlier detection

2016 International Journal of Latest Trends in Engineering and Technology  
In GIS there can be many problems that include finding the nearest neighbor or finding the outliers in a data set.  ...  In SDBMS, objects are defined in a geometrical shape such as points, lines and polygons. Spatial database offers spatial data types, data models and query languages to process the spatial data.  ...  Outliers can be found in biological data for finding any type of medical change in the body.  ... 
doi:10.21172/1.81.004 fatcat:i3zv2lin5navvh4safdbkr6gey

A Model for Spatial Outlier Detection Based on Weighted Neighborhood Relationship [article]

Ayman Taha, Hoda M.Onsi, Mohammed Nour El din, Osman M. Hegazy
2019 arXiv   pre-print
In this paper, we propose a model to redefine the spatial neighborhood relationship by considering weights of the most effective parameters of neighboring objects in a given spatial data set.  ...  Spatial outliers are used to discover inconsistent objects producing implicit, hidden, and interesting knowledge, which has an effective role in decision-making process.  ...  If we compare the results of the two models, we find that the next objects {17, 216, 238, 26, 317, 30} are spatial outliers in the two models.  ... 
arXiv:1911.01867v1 fatcat:cds7itnh6rclrnekuy6abicf7m

Outlier Detection for Temporal Data: A Survey

Manish Gupta, Jing Gao, Charu C. Aggarwal, Jiawei Han
2014 IEEE Transactions on Knowledge and Data Engineering  
In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which  ...  In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data  ...  OUTLIER DETECTION FOR STREAM DATA Evolving Prediction Models Given: A multidimensional data stream s Find: Outlier points in s Evolving prediction models are models in which the parameters or components  ... 
doi:10.1109/tkde.2013.184 fatcat:b6nableuvvgthlw3xxj6axabgi


Winda Chairani Mastuti, Anik Djuraidah, Erfiani Erfiani
2019 Indonesian Journal of Statistics and Its Applications  
One of this method is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model.  ...  Detecting and accommodating outlier is an important step of the regression analysis. Several methods can detect outliers in spatial regression.  ...  The robust spatial autoregressive model (RSAR) can be used to modelling data that contain outliers.  ... 
doi:10.29244/ijsa.v4i1.573 fatcat:ehhziinfzzelhpcd7pjxy2qblu

Detecting graph-based spatial outliers

Shashi Shekhar, Chang-Tien Lu, Pusheng Zhang
2001 Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '01  
In this paper, we focus on detecting spatial outliers in graph structured data sets.  ...  Existing methods are designed for detecting spatial outliers in multidimensional geometric data sets, where a distance metric is available.  ...  CONCLUSIONS In this paper, we focused on detecting outliers in spatial graph data sets.  ... 
doi:10.1145/502512.502567 fatcat:2wzk32lfpnchdgvqloxsxtnfsy

Automated Sensor Verification Using Outlier Detection in the Internet of Things

Nauman Javed, Tilman Wolf
2012 2012 32nd International Conference on Distributed Computing Systems Workshops  
This model is then used to compare sensor readings and to identify outliers through spatial and temporal interpolation.  ...  In this paper, we address the problem of trust -specifically how to verify sensor information that is gathered from multiple sensors that are managed by different entities using outlier detection.  ...  ACKNOWLEDGEMENTS We would like to thank Xianglong Kong for collecting the weather data used in this work.  ... 
doi:10.1109/icdcsw.2012.78 dblp:conf/icdcsw/JavedW12 fatcat:6mlrf7cxy5b6hph3uqat7rix4i

Importance of Outlier Detection in Spatial Analysis of Wind Erosion

Mohsen Hosseinalizadeh, Firoozeh Rivaz, Roya Hedayatizadeh
2011 Procedia Environmental Sciences  
The aim of this paper is to investigate the influence of outliers on variogram model and their parameters in east of Iran.  ...  Spatial outliers in wind erosion are associated with severe weather events so , high rate of erosion or sedimentation in relevance to their spatial vicinity is called outlier.  ...  model. 5.Reference 3.a: all of data, b: all of data without outliers Spatial plot of data of interest in different cases is illustrated in[Fig.4].  ... 
doi:10.1016/j.proenv.2011.07.059 fatcat:vvxw4qhyvbfgpjuk7iiufltceu

Outlier Detection from Non-Smooth Sensor Data

Timo Huuhtanen, Henrik Ambos, Alexander Jung
2019 2019 27th European Signal Processing Conference (EUSIPCO)  
In this paper, we propose novel approaches for outlier detection of non-smooth spatial data.  ...  Outlier detection is usually based on smooth assumption of the data. Most existing approaches for outlier detection from spatial sensor data assume the data to be a smooth function of the location.  ...  For spatial data, a widely used signal model is smoothness in the sense of requiring similar values for data points at close-by locations.  ... 
doi:10.23919/eusipco.2019.8903061 dblp:conf/eusipco/HuuhtanenAJ19 fatcat:vhwlpqvw3bc5fmdg6xr3qegzhe

Spatio-Temporal Outlier Detection in Large Databases

Derya Birant, Alp Kut
2006 Journal of Computing and Information Technology  
In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with the remaining large amount of data.  ...  In contrast to the existing outlier detection algorithms, the new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects.  ...  They deploy some standard distribution model (e.g. Normal, Poisson, etc.) and recognize as outliers those points which deviate from the model.  ... 
doi:10.2498/cit.2006.04.04 fatcat:cd67tm64kveupb54gwt6v755dy

Spatio-temporal outlier detection in large databases

D. Birant, A. Kut
2006 28th International Conference on Information Technology Interfaces, 2006.  
In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with the remaining large amount of data.  ...  In contrast to the existing outlier detection algorithms, the new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects.  ...  They deploy some standard distribution model (e.g. Normal, Poisson, etc.) and recognize as outliers those points which deviate from the model.  ... 
doi:10.1109/iti.2006.1708474 fatcat:hsqxjyulk5ajdazoywmon7i324

Outlier detection special issue

Sanjay Chawla, David Hand, Vasant Dhar
2010 Data mining and knowledge discovery  
Thus the emphasis has been on the design of algorithms to find outliers in complex scenarios while relaxing as many assumptions on the underlying data generating model as possible.  ...  The objective of Outlier Detection in Data Mining is in similar vein-outliers often embody new information, which is often hard to explain in the context of existing knowledge and results in a re-evaluation  ...  We hope the readers will find these papers beneficial and that this special issue will catalyse further interest in this important area of Data Mining.  ... 
doi:10.1007/s10618-009-0163-0 fatcat:oaekiiwzzndztmcirca6ykggrq

Two Level Data Fusion Model for Data Minimization and Event Detection in Periodic Wireless Sensor Network

Neetu Verma, Dinesh Singh
2020 International Journal on AdHoc Networking Systems  
This Model is also used to accurately detect early events in case of Emergency.  ...  Therefore, Two Level Data Fusion Model is proposed in this paper to transfer minimized data with the ability of accurately determines the event with minimum Delay.  ...  The proposed data fusion model utilizes both spatial and temporal correlation for transferring minimized data as well as finding accurate event to the sink node.  ... 
doi:10.5121/ijans.2020.10302 fatcat:czqrvulgofcs7hbsdt7h52ecp4

Alcohol retail sales licenses and sexually transmitted infections in Texas counties, 2008-2015

Matthew E. Rossheim, Jenna R. Krall, Julia E. Painter, Dennis L. Thombs, Caroline J. Stephenson, Sumihiro Suzuki, M. Brad Cannell, Melvin D. Livingston, Kwynn M. Gonzalez-Pons, Alexander C. Wagenaar
2018 Figshare  
Associations between retail alcohol sales licenses and STIs were estimated using spatial linear models and Poisson mixed effects models for over-dispersed count data.  ...  In Poisson models, each additional on-premise (e.g., bar and restaurant) alcohol license per 10,000 population in a county was associated with a 1.5% increase (95% CI: 0.4%, 2.6%) in the rate of HIV and  ...  effects models led to similar findings as results from spatial linear models.  ... 
doi:10.6084/m9.figshare.6429623.v1 fatcat:qdmsoeuwlnefbdk5lmai6pfpli
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