Mining quantitative association rules in large relational tables

SrikantRamakrishnan,; AgrawalRakesh,

doi:10.1145/235968.233311

Cited by 511 publications

(71 citation statements)

References 3 publications

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“…metric distance), while the conventional association rule mining can only deal with categorical (classified) data. A solution to this problem is that we, first classify numeric data into ordinal categories and then mine these ordinal data for association rules (PiatetskyShapiro, 1991;Srikant and Agrawal, 1996). For example, metric distance may be categorized into 'very near', 'near', 'medium, and 'far'.…”

Section: Spatial Association Rule Miningmentioning

confidence: 99%

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Karimipour¹,

Kanani‐Sadat²

2016

J ENVIRON INFORM

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ABSTRACT. The prevalence of allergic diseases has highly increased in recent decades due to contamination of the environment with the allergy stimuli. A common treat is identifying the allergy stimulus, and then avoiding the patient to be exposed with it. There are, however, many unknown allergic diseases stimuli that are related to the characteristics of the living environment. In this article, we focus on the effect of air pollution on asthmatic allergies and investigate the association between prevalence of such allergies with those characteristics of the environment that may affect the air pollution. This investigation, eventually, leads to map the vulnerability of asthmatic allergy prevalence based on environmental characteristics. For this, spatial association rule mining has been deployed to mine the association between spatial distribution of allergy prevalence and the air pollution parameters such as CO, SO2, NO2, PM10, PM2.5, and O3 (compiled by the air pollution monitoring stations) as well as living distance to parks and roads. The categories of attributes have been defined as fuzzy sets in order to handle the data uncertainty. The results for the case study (i.e., Tehran metropolitan area) indicates that distance to parks and roads as well as CO, NO2, PM10, and PM2.5 is related to the allergy prevalence in December (the most polluted month of the year in Tehran), while SO2 and O3 have no effect on that. In June, however, the distance to parks and roads as well as NO2, PM10, and PM2.5 affect the allergy prevalence, but CO, SO2 and O3 are ineffective.

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Section: Spatial Association Rule Miningmentioning

confidence: 99%

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Karimipour¹,

Kanani‐Sadat²

2016

J ENVIRON INFORM

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“…This approach raises several questions, from the choice of the number of buckets to the size of the resulting data. A more elegant approach was provided by Srikant and Agrawal [16], who presented a machinery that solves most of the problems automatically. Their method is still based on a priori bucketing, and moreover, it is very specific to association rule mining, making it hard (or impossible) to apply to redescription mining.…”

Section: Data Discretizationmentioning

confidence: 99%

From black and white to full color: extending redescription mining outside the Boolean world

Galbrun

Miettinen

2012

Statistical Analysis

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Redescription mining is a powerful data analysis tool that is used to find multiple descriptions of the same entities. Consider geographical regions as an example. They can be characterized by the fauna that inhabits them on one hand and by their meteorological conditions on the other hand. Finding such redescriptors, a task known as niche‐finding, is of much importance in biology. Current redescription mining methods cannot handle other than Boolean data. This restricts the range of possible applications or makes discretization a pre‐requisite, entailing a possibly harmful loss of information. In niche‐finding, while the fauna can be naturally represented using a Boolean presence/absence data, the weather cannot. In this paper, we extend redescription mining to categorical and real‐valued data with possibly missing values using a surprisingly simple and efficient approach. We provide extensive experimental evaluation to study the behavior of the proposed algorithm. Furthermore, we show the statistical significance of our results using recent innovations on randomization methods. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012

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“…This information is provided by OLAP based on the MP B1 that presents the aspects of physicians characterized by their specialization, age, gender, workload, location where they work, and dispersion (see Figure 1). On the other hand, MP B2 is aimed at discovering interesting relations in physicians' data by an association rules discovery method (Srikant and Agrawal, 1996). In this model these rules show characteristics of physicians in some municipalities.…”

Section: Physiciansmentioning

confidence: 99%

Primary health‐care network monitoring: a hierarchical resource allocation modeling approach

Pur

Bohanec

Lavrač

et al. 2010

Health Planning & Management

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Management of a primary health-care network (PHCN) is a difficult task in every country. A suitable monitoring system can provide useful information for PHCN management, especially given a large quantity of health-care data that is produced daily in the network. This paper proposes a methodology for structured development of monitoring systems and a PHCN resource allocation monitoring model based on this methodology. The purpose of the monitoring model is to improve the allocation of health-care resources. The proposed methodology is based on modules that are organized into a hierarchy, where each module monitors a particular aspect of the system. This methodology was used to design a PHCN monitoring model for Slovenia. Specific aspects of the Slovenian PHCN were taken into account such as varying needs of patients from different municipalities, existence of small municipalities having less than 1000 residents, the fact that many patients visit physicians in other municipalities, and that physicians may work at more than one location or organization. The main modules in the model are focused on the overall assessment of the PHCN, monitoring of patients visits to health-care providers (HCPs), physical accessibility of health services, segment of patients in municipalities who have not selected a personal physician, assessment of the availability of HCPs for patients, physicians working on more than one location, and available human resources in the PHCN. Most of the model's components are general and can be adapted for other national health-care systems.

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Mining quantitative association rules in large relational tables

Cited by 511 publications

References 3 publications

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

Mapping the Vulnerability of Asthmatic Allergy Prevalence Based on Environmental Characteristics through Fuzzy Spatial Association Rule Mining

From black and white to full color: extending redescription mining outside the Boolean world

Primary health‐care network monitoring: a hierarchical resource allocation modeling approach

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