Finding Main Causes of Elevator Accidents via Multi-Dimensional Association Rule in Edge Computing Environment

In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.

Mining multilevel spatial association rules with cloud models

The traditional generalization-based knowledge discovery method is introduced. A new kind of multilevel spatial association of the rules mining method based on the cloud model is presented. The cloud model integrates the vague and random use of linguistic terms in a unified way. With these models, spatial and nonspatial attribute values are well generalized at multiple levels, allowing discovery of strong spatial association rules. Combining the cloud model based method with Apriori algorithms for mining association rules from a spatial database shows benefits in being effective and flexible.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Spatial Multidimensional Association Rules Mining in Forest Fire Data

Hotspots (active fires) indicate spatial distribution of fires. A study on determining influence factors for hotspot occurrence is essential so that fire events can be predicted based on characteristics of a certain area. This study discovers the possible influence factors on the occurrence of fire events using the association rule algorithm namely Apriori in the study area of Rokan Hilir Riau Province Indonesia. The Apriori algorithm was applied on a forest fire dataset which containeddata on physical environment (land cover, river, road and city center), socio-economic (income source, population, and number of school), weather (precipitation, wind speed, and screen temperature), and peatlands. The experiment results revealed 324 multidimensional association rules indicating relationships between hotspots occurrence and other factors.The association among hotspots occurrence with other geographical objects was discovered for the minimum support of 10% and the minimum confidence of 80%. The results show that strong relations between hotspots occurrence and influence factors are found for the support about 12.42%, the confidence of 1, and the lift of 2.26. These factors are precipitation greater than or equal to 3 mm/day, wind speed in [1m/s, 2m/s), non peatland area, screen temperature in [297K, 298K), the number of school in 1 km2 less than or equal to 0.1, and the distance of each hotspot to the nearest road less than or equal to 2.5 km.

Research on spatial association rules mining in two-direction

In data mining from transaction DB, the relationships between the attributes have been focused, but the relationships between the tuples have not been taken into account. In spatial database, there are relationships between the attributes and the tuples, and most of the associations occur between the tuples, such as adjacent, intersection, overlap and other topological relationships. So the tasks of spatial data association rules mining include mining the relationships between attributes of spatial objects, which are called as vertical direction DM, and the relationships between the tuples, which are called as horizontal direction DM. This paper analyzes the storage models of spatial data, uses for reference the technologies of data mining in transaction DB, defines the spatial data association rule, including vertical direction association rule, horizontal direction association rule and two-direction association rule, discusses the measurement of spatial association rule interestingness, and puts forward the work flows of spatial association rule data mining. During two-direction spatial association rules mining, an algorithm is proposed to get non-spatial itemsets. By virtue of spatial analysis, the spatial relations were transferred into non-spatial associations and the non-spatial itemsets were gotten. Based on the non-spatial itemsets, the Apriori algorithm or other algorithms could be used to get the frequent itemsets and then the spatial association rules come into being. Using spatial DB, the spatial association rules were gotten to validate the algorithm, and the test results show that this algorithm is efficient and can mine the interesting spatial rules.

Association Rule Discovery and Its Applications

Data mining, i.e., mining knowledge from large amounts of data, is a demanding field since huge amounts of data have been collected in various applications. The collected data far exceed people's ability to analyze it. Thus, some new and efficient methods are needed to discover knowledge from large database. Association rule discovery is an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent item sets and then forming conditional implication rules among them. In this paper, we describe and summarize recent work on association rule discovery, offer a new method to association rule mining and point out that association rule discovery can be applied in spatial data mining. It is useful to discover knowledge from remote sensing and geographical information system.

Painting image browser applying an associate-rule-aware multidimensional data visualization technique

Exploration of artworks is enjoyable but often time consuming.For example,it is not always easy to discover the favorite types of unknown painting works.It is not also always easy to explore unpopular painting works which looks similar to painting works created by famous artists.This paper presents a painting image browser which assists the explorative discovery of user-interested painting works.The presented browser applies a new multidimensional data visualization technique that highlights particular ranges of particular numeric values based on association rules to suggest cues to find favorite painting images.This study assumes a large number of painting images are provided where categorical information(e.g.,names of artists,created year)is assigned to the images.The presented system firstly calculates the feature values of the images as a preprocessing step.Then the browser visualizes the multidimensional feature values as a heatmap and highlights association rules discovered from the relationships between the feature values and categorical information.This mechanism enables users to explore favorite painting images or painting images that look similar to famous painting works.Our case study and user evaluation demonstrates the effectiveness of the presented image browser.

乌鲁木齐市中心城区土地多功能利用空间分异研究

研究基于POI等城市多源数据,采用空间区位指数法、空间自相关分析法及关联规则挖掘法,探究中心城区土地多功能利用的空间分布特征与差异,得出土地多功能利用类型的空间关联规则。结果表明:(1)中心城区土地多功能利用空间分布特征中,单一功能广泛散布在城区外围,由市区中部呈现出“混合功能—双功能—单一功能”占位发展与圈层扩张模式。(2)在中心城区土地多功能利用空间差异分析中,生活、生产—生活、混合功能区功能为低水平区位熵值,生态、生产功能为中等水平区位熵值,生产—生态、生活—生态功能为高水平区位熵值。以生产功能与生活功能为主导的生产、生活、生产—生活、生活—生态及混合功能为正相关类型,以生态功能为主导的生态功能和生产—生态功能为负相关类型,且具有明显的边缘效应。(3)根据关联规则分析,生产、生活、混合及生产—生活功能之间具有高强度的相互关联性,生活—生态功能和生产—生态功能与其关联性为中等强度,而生态功能与其他六种功能的关联强度是最薄弱的。

从GIS数据库中挖掘空间关联规则研究

G IS数据库中空间关联规则发现是SDMKD的重要内容,广泛涉及到知识的表示和推理,需要地理空间知识的深入参与。在地理空间认知的基础上,结合认知逻辑,通过ILP对空间关联规则进行形式化描述,特别分析了其中涉及的空间谓词;通过例子说明了形式化空间关联规则的具体应用。从G IS数据库中挖掘空间关联规则的主要问题是多层、多关系的规则挖掘问题,不同专题图层不同空间对象之间空间谓词的高效计算与存储表达是解决问题的关键;把空间关系非空间化,将连续数据离散化,从而把求解问题转换成布尔型关联规则问题进行讨论,基于此而探讨了一种通过SJI-P表组织空间谓词,然后根据目标对象的概念层级自顶向下、逐层细化的空间关联规则挖掘方法。

基于空间分析的空间关联规则提取

空间关联规则是空间数据挖掘的一个重要结果表现形式，从GIS中挖掘空间关联 规则是一个理解和将GIS中的数据转化成知识很好的方法。GIS从本质上讲是一个空间数据库 管理系统，现在的商业空间数据库管理系统中都不能存储空间分析的关系如空间拓扑关系、 空间相邻关系等。文章提出了一种基于空间分析的空间关联规则的挖掘算法，并给出了实例 、分析及讨论。

用语言云模型发掘关联规则(英文)

该文提出用语言云模型用于 KDD中知识表达和不确定性处理 ,引入了多维云模型作为一维模型的扩展 .语言云的数字特征量将语言值的模糊性和随机性用统一的方式巧妙地综合到一起 ,基于云模型的概念层次结构可以跨越定量和定性知识之间的鸿沟 .为了发现强关联规则 ,属性值要在较高的概念层上泛化 ,同时允许相邻属性值或语言项间有重叠 .这种软划分可以模仿人类的思想 ,使发现的知识具有稳健性 .将基于云模型的泛化方法与 Apriori算法结合起来 ,从空间数据库中发掘关联规则 .试验显示了其有效性、高效性和灵活性 .

空间关联规则挖掘研究进展

随着空间数据获取技术的进步,空间数据量日益增大,已超出人们的分析能力。传统的空间数据分析方法只能进行简单的数据分析,无法满足人们获取知识的需要。空间关联规则是空间数据挖掘一个基本的任务,是从具有海量、多维、多尺度、不确定性边界等特性的空间数据中进行知识发现的重要方法。本文从基本概念、分类、挖掘过程、挖掘方法、目前研究成果等方面对其进行综述,重点阐述了空间关联规则挖掘效率的改进策略、基于不确定空间信息的挖掘方法、挖掘过程及结果的可视化、弱空间关联规则的挖掘方法等。通过对现有空间关联规则研究成果和存在问题的深入剖析,指出了其未来主要的发展方向。

用Apriori关联规则挖掘算法发现湖北电网雷击灾害的时空分布规律

根据湖北电网多年雷击故障数据与雷击监测数据,探究雷电活动的日分布、月分布、高程分布、二维空间分布以及时空尺度结合的分布特征,发现湖北地区雷电活动频繁的月份主要为每年七八月间,在一天之中主要集中在15:00～18:00时段;地域上主要高发于湖北黄石、咸宁东部、武汉中部、宜昌中东部、襄樊南部及北部少数地区.用Apriori关联规则对多源融合雷击数据进行挖掘得到的结果表明,输电线周围天气满足以下条件时易发生雷击故障,需加强防范:日平均相对湿度大于80%,日平均气压高于1×10~5Pa,日平均水汽压在3×10~3～4×10~3Pa之间.利用大数据关联规则对输电线周围雷击进行数据挖掘和分析可对输电线路运营规划提供指导,为电网雷击灾害防范提供决策辅助.

空间关联规则的双向挖掘

空间数据库中关联规则挖掘不仅需要考虑关系元组属性之间的关系———纵向关系,更需要挖掘元组之间的关系———横向关系,如相邻、相交、重叠等。本文通过分析空间数据库的存储模式,借鉴事务数据库关联规则的挖掘方法,对空间关联规则进行完整定义,并对规则的兴趣度度量进行探讨。根据挖掘的方向将空间数据挖掘归纳为纵向挖掘、横向挖掘、双向挖掘。在双向挖掘中,提出一种新算法,该算法根据挖掘任务进行约束,缩小挖掘空间,然后通过空间计算将空间关系转化为非空间关系,经过多次循环,获取非空间项集,进而挖掘出空间关联规则。据此提出空间数据双向挖掘工作流程,并通过实例进行了验证。

一个适用于地理信息系统的数据挖掘工具—GISMiner

论文将常用于关系型、事务型数据库的面向属性归纳的方法和关联规则挖掘方法扩展至空间数据库 ,开发了适用于地理信息系统 (GIS)的空间数据挖掘工具———GISMiner,并将其应用于从农田GIS中挖掘农田使用情况的空间特征规则 ,以及从农产品市场GIS中挖掘农产品价格与铁路、国道和河流间的空间关联规则实验 .结果表明 ,GISMiner是一个实用的。

图象数据关联规则挖掘

本文在介绍了多媒体数据挖掘基本思想和图象数据主要特征基础之上 ,对图象数据关联规则挖掘进行了讨论 .首先对关联规则挖掘有关问题加以描述和分析 ,针对图象数据 ,定义了新的关联规则及其有关概念 ,并给出考虑项数量的原子特征和空间关系关联规则挖掘算法 Max Occur和 MAR- Spatial。

基于J2EE的空间数据挖掘系统设计与实现

在分析空间数据挖掘特点的基础上,充分集成空间数据仓库技术、空间数据挖掘技术以及空间信息表达等技术,设计了一个基于J2EE的空间数据挖掘原型。重点介绍该原型系统的功能框架与体系结构、空间关联规则挖掘模块、挖掘结果的可视化表达模块的设计和实现办法。最后给出系统以某市土地利用现状数据集为例的空间关联规则挖掘结果界面。结果表明该系统可较好地满足可靠性、扩展性、可用性等业务需要。

元规则指导下的逐步求精多层空间关联规则挖掘算法

提出了元规则指导下逐步求精的多层空间关联规则挖掘算法，该算法采用了自顶向下、逐步求精以及元规则等技术。文章最后以挖掘农产品市场的芝麻价格与铁路、国道和河流间的空间关联关系为例，介绍此算法的应用。

内蒙古河套灌区沈乌灌域生态系统健康评价和时空变化分析

基于生态健康理论和景观生态学理论,使用土地利用和遥感数据,采用空间自相关分析和空间关联规则挖掘方法,分析内蒙古河套灌区沈乌灌域2000—2015年生态系统健康的时空变化。结果表明:2000—2015年沈乌灌域生态系统健康水平呈先缓慢提高、再迅速提高的态势,这与该区域土地利用结构变化由沙地转变为耕地、草地和林地以及由建设用地转变为耕地和草地密切相关。2000,2015年的Moran's I分别为0.627,0.606,空间集聚现象显著,高生态健康区分布在西部、中部和东部呈南北走向的水域湿地范围内,低生态健康区主要分布在北部、中部和南部的沙地以及东南角的城镇建成区。2000—2015年区域生态健康空间格局未发生显著变化。干渠近距离范围内基本不存在低生态健康区,区域内的灌溉设施对生态健康格局有重要影响。

基于Web的空间关联规则提取及可视化的实现

提出了一种基于空间拓扑概念树的关联规则挖掘算法,基于J2EE设计了一个B/S模式的关联规则挖掘工具,给出了W eb三维空间关联规则可视化的实现流程,最后将该技术应用于两个具体实例中,并给出实现的运行界面。