A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases for Swiss Population using Data Mining Methods

Background: This article demonstrates that using data mining methods such as association analysis on an integrated Swiss database derived from a Swiss national dietary survey (menuCH) and Swiss demographical and health data is a powerful way to determine whether a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. Objective: The objective of the study was to use an integrated database of dietary and health data from a large group of Swiss population to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food con-sumption. Design: Food consumption databases from a Swiss national survey menuCH were gathered along with corresponding large survey of demographics and health data from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and reported in a previous study as a single integrated database. A data mining method such as A-priori association analysis was applied to this integrated database. Results: Association mining analysis was used to incorporate rules about food consumption and lifestyle diseases. A set of promising preliminary rules and their corresponding interpretation was generated, which is reported in this paper. As an example, the found rules of the sample show that smoking is relatively irrelevant to the high blood pressure and Diabetes, whereas consuming vegetables at regular basis reduces the risk of high Cholesterol. Conclusions: Association rule mining was successfully used to describe and predict rules linking food consumption patterns with lifestyle diseases. The gained association rules reveal that the appearance of the mutually independent nutritional characteristics in the rules are equally distributed.Furthermore, most of the sample show no chronic diseases as they smoke little and exercise regularly, which can be interpreted that sport is a strong preventive factor for chronic/lifestyle diseases. Nevertheless, a small percentage of the sample shows chronic illnesses due to unhealthy eating. Further research should consider the weighting of chronic diseases’ characteristics for them not to be pruned out early by data mining computation

Finding Correlation between Chronic Diseases and Food Consumption from 30 Years of Swiss Health Data Linked with Swiss Consumption Data using FP-Growth for Association Analysis

Objective: The objective of the study was to link Swiss food consumption data with demographic data and 30 years of Swiss health data and apply data mining to discover critical food consumption patterns linked with 4 selected chronical diseases like alcohol abuse, blood pressure, cholesterol, and diabetes. Design: Food consumption databases from a Swiss national survey menuCH were gathered along with data of large surveys of demographics and health data collected over 30 years from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and Frequent Pattern Growth (FP-Growth) for the association rule mining was applied to the integrated database. Results: This study applied data mining algorithm FP-Growth for association rule analysis. 36 association rules for the 4 investigated chronic diseases were found. Conclusions: FP-Growth was successfully applied to gain promising rules showing food consumption patterns lined with lifestyle diseases and people's demographics such as gender, age group and Body Mass Index (BMI). The rules show that men over 50 years consume more alcohol than women and are more at risk of high blood pressure consequently. Cholesterol and type 2 diabetes is found frequently in people older than 50 years with an unhealthy lifestyle like no exercise, no consumption of vegetables and hot meals and eating irregularly daily. The intake of supplementary food seems not to affect these 4 investigated chronic diseases

Characterizing Thermal Energy Consumption through Exploratory Data Mining Algorithms

Nowadays large volumes of energy data are continuously collected through a variety of meters from dierent smart-city environments. Such data have a great potential to influence the overall energy balance of our communities by optimizing building energy consumption and by enhancing people's awareness of energy wasting. This paper presents FARTEC, a data mining engine based on exploratory and unsupervised data mining algorithms to characterize building energy consumption together with meteorological conditions. FARTEC exploits a joint approach coupling cluster analysis and association rules. First, a partitional clustering algorithm is applied to weather conditions to discover groups of thermal energy consumption that occurred in similar weather conditions. Each computed cluster is then locally characterized through a set of association rules to ease the manual inspection of the most interesting correlations between thermal consumption and weather conditions. FARTEC also includes a categorization of the rules into a few groups according to their meaning. Each group is determined by the data features appearing in the rule. The experimental evaluation performed on real datasets demonstrates the effectiveness of the proposed approach in discovering interesting knowledge items to raise people's awareness of their energy consumption

Discovery of Association Rules of the Relationship between Food Consumption and Life Style Diseases From Swiss Nutrition’s (menuCH) Dataset & Multiple Swiss Health Datasets from 1992 To 2012

This article demonstrates that using data mining methods such as Weighted Association Rule Mining (WARM) on an integrated Swiss database derived from a Swiss national dietary survey (menuCH) and 25 years of Swiss demographical and health data is a powerful way to determine whether a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. The objective of the study was to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Food consumption databases from a Swiss national survey menuCH were gathered along with data of large surveys of demographics and health data collected over 25 years from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and reported in a previous study as a single integrated database. A data mining method such as WARM was applied to this integrated database. A set of promising rules and their corresponding interpretation was generated. As an example, the found rules of the sample show that the consumption of alcohol in small quantities does not have a negative impact on health, whereas the consumption of vegetables is important for the supply of vitamins of the B group, which help the energy metabolism to provide energy. These vitamins are particularly lacking in alcoholics and should then be taken with supplements. Another finding is that dietary supplements do little specially by diabetes. Applying WARM algorithm was beneficial for this study since no interesting rules were pruned out early and the significance of the rules could be highly increased as compared to a previous study using pure Apriori Algorithm

Optimization Method for the Chiller plant of Central Air-conditioning System Parameters on Association Rules Analysis for Energy Conservation

More than 50% of the total energy consumption of central air-conditioning system is consumed by the central cooling plants. It is crucial to optimize central cooling plants operation parameter settings which is also significant for improving its operating efficiency, reducing the energy consumption, and promoting the overall energy saving of air-conditioning systems. The regular methods of central cooling plants optimization can be divided into three categories: engineering method, mechanism modeling and artificial intelligence modeling. In recent years, with the development of the internet of things, the monitoring and control platform for air-conditioning system provides data mining with mass ground truth data for central cooling plants optimization. Compared with the other methods, the data mining method for optimizing the key operation parameters of central cooling plants takes the advantages of simple, wide applicable and practical. In this paper, the association rule data mining method is proposed to optimize the operation parameters of the whole central cooling plants from the ground truth data. The central cooling plants in a shopping mall in Guangzhou is taken as the case study. Through historical data processing, like data cleaning, selection of optimization parameters, discrete transform of data and so on, the association rules are mined between the optimal energy efficiency Ratio and the running parameters of the central cooling plants under different operating conditions by Apriori algorithm. Finally, from the simulation results, it’s shown that by the association rules, the total energy consumption of the whole central cooling plants under two different working conditions are reduced 13.33% and 11.6% less than by the original operational parameters in the transition season and summer respectively. The simulation results verify the validity of the mining rules. This method excavates the energy saving potential of central cooling plants from the point of view of engineering practice, which is suitable for the central cooling plants which has accumulated a large amount of operation data and provides a reference for the energy-saving optimization operation of central cooling plants

TÉCNICAS DE MINERÍA DE DATOS COMO SOPORTE PARA LA GESTIÓN DE UN SISTEMA DE COMERCIALIZACIÓN DE ENERGÍA ELÉCTRICA

Tener un suministro de energía eléctrica suficiente es vital para la comunidad, lo que demanda mantenimiento y mejora continua del servicio por parte de las compañías prestatarias del servicio. Entre otros aspectos, estas compañías mantienen bases de datos que capturan el consumo de la energía y en tal sentido en la presente investigación se propone el uso de técnicas de Redes Neuronales Artificiales y Reglas de Asociación como soporte a la gestión del sistema de comercialización de la energía eléctrica en una empresa pública de la ciudad de Manta, a partir de una muestra de datos extraídos de las facturas de consumo residencial correspondientes al año 2015. Los algoritmos usados específicamente fueron el perceptrón multicapa a nivel de redes neuronales y PART como regla de asociación. En esta aplicación empírica de minería de datos, se demostró que las redes neuronales y reglas de asociación son alternativas viables para predecir los niveles de consumo y comprender los patrones de consumo de energía.PALABRAS CLAVE: Redes neuronales; Reglas de Asociación; Datamining; minería de dato; WEKA; Consumo de energía eléctrica.TECHNIQUES OF DATA MINING AS SUPPORT FOR THE MANAGEMENT OF A SYSTEM OF ELECTRIC ENERGY COMMERCIALIZATIONABSTRACTHaving a sufficient electrical power supply is vital for the community, which demands maintenance and continuous improvement of the service by the service companies. Among other aspects, these companies maintain data bases that capture energy consumption and in this sense the present research proposes the use of Artificial Neural Network techniques and Association Rules as support for the management of the marketing system of the electric power in a public company of Manta city, based on a sample of data extracted from residential consumption bills for the year 2015. The algorithms used specifically were the multilayer perceptron at the level of neural networks and PART as a rule of association. In this empirical application of data mining, it was shown that neural networks and association rules are viable alternatives to predict consumption levels and to understand energy consumption patterns.KEYWORDS: Neural Networks; Association Rules; Datamining; Data Mining; WEKA; Electric Power Consumption

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Association Rule Mining on Big Data Sets

An accurate, complete, and rapid establishment of customer needs and existence of product recommendations are crucial points in terms of increasing customer satisfaction level in various different sectors such as the banking sector. Due to the significant increase in the number of transactions and customers, analyzing costs regarding time and consumption of memory becomes higher. In order to increase the performance of the product recommendation, we discuss an approach, a sample data creation process, to association rule mining. Thus instead of processing whole population, processing on a sample that represents the population is used to decrease time of analysis and consumption of memory. In this regard, sample composing methods, sample size determination techniques, the tests which measure the similarity between sample and population, and association rules (ARs) derived from the sample were examined. The mutual buying behavior of the customers was found using a well-known association rule mining algorithm. Techniques were compared according to the criteria of complete rule derivation and time consumption