Association Rules in Data Mining: An Application on a Clothing and Accessory Specialty Store

Retailers provide important functions that increase the value of the products and services they sell to consumers. Retailers value creating functions are providing assortment of products and services: breaking bulk, holding inventory, and providing services. For a long time, retail store managers have been interested in learning about within and cross-category purchase behavior of their customers, since valuable insights for designing marketing and/or targeted cross-selling programs can be derived. Especially, parallel to the development of information processing and communication technologies, it has become possible to transfer customers shopping information into databases with the help of barcode technology. Data mining is the technique presenting significant and useful information using of lots of data. Association rule mining is realized by using market basket analysis to discover relationships among items purchased by customers in transaction databases. In this study, association rules were estimated by using market basket analysis and taking support, confidence and lift measures into consideration. In the process of analysis, by using of data belonging to the year of 2012 from a clothing and accessory specialty store operating in the province of Osmaniye, a set of data related to 42.390 sales transactions including 9.000 different product kinds in 35 different product categories (SKU) were used. Analyses were carried out with the help of SPSS Clementine packet program and hence 25.470 rules were determined

Efficiency Measurement in Turkish Coal Enterprises Using Data Envelopment Analysis and Data Mining

Gradual population growths, skyrocketing technological developments and inter-State competitions increase the energy demands continuously. Although the countries try to diverse their energy sources in order to sustain their developments, they also have to pay attention to protect their energy independences. Thus, it is very important to develop their self-resources. Coal is the most common natural source which can meet our energy needs. However, coal mine enterprises have to be administrated cost-effectively in order get minimum energy costs. In this study, the efficiency of Turkish coal enterprises between the years 2003-2010 is measured by using Data Envelopment Analysis (DEA). Then, indicators which are the most important in estimating the efficiency were determined by using the efficiency scores obtained by DEA in the Data Mining technique

Determination of Ra-226 concentration in bottled mineral water and assessment of effective doses, a survey in Turkey

Background: There is a rapid increase worldwide in the consumption of mineral waters which may contain different level of radioactive elements, especially, Ra-226 K in addition to varying amounts of beneficial salts. Therefore, a comprehensive study was planned and carried out in order to determine concentration of Ra-226 natural radionuclide in bottled mineral waters that commercially available in Marmara Region of Turkey. Materials and Methods: The method used for Ra-226 concentration analysis bases on the measurement of Radon (Rn-222) coming from Ra-226 dissolved in the water. The measurements were performed using RAD 7, a solid state a detector, with RAD H2O accessory manufactured by DURRIDGE COMPANY Inc. Results: The Ra-226 concentration in mineral waters was found to vary from <0.074 to 0.625 Bql(-1) with an average value of 0.267 Bql(-1). The committed effective doses due to ingestion of Ra-226 from the one year consumption of these waters were estimated to range from 10.8 to 90 mu Svy(-1), from 9 to 75 mu Svy(-1) and from 3.15 to 26.25 mu Svy(-1), for infants, children and adults, respectively. Conclusion: The results obtained in this study indicate that the committed effective doses are below the WHO (World Health Organization) recommended reference level of 100 mu Svy(-1)

Radon measurements in water samples from the thermal springs of Yalova basin, Turkey

The radon concentration has been measured in thermal waters used for medical therapy and drinking purposes in Yalova basin, Turkey. Radon activity measurements in water samples were performed using RAD 7 radon detector equipped with RAD H2O (radon in water) accessory and following a protocol proposed by the manufacturer. The results show that the concentration of Rn-222 in thermal waters ranges from 0.21 to 5.82 Bql(-1) with an average value of 2.4 Bql(-1). In addition to radon concentration, physicochemical parameters of water such as temperature (T), electrical conductivity, pH and redox potential (E-h) were also measured. The annual effective doses from radon in water due to its ingestion and inhalation were also estimated. The annual effective doses range from 0.2 to 0.75 mu Svy(-1) for ingestion of radon in water and from 2.44 to 9 mu Svy(-1) for inhalation of radon released from the water

İŞLETMELERİN FİNANSALBAŞARISIZLIĞININ VERİ MADENCİLİĞİ VE DİSKRİMİNANT ANALİZİ MODELLERİ İLE TAHMİN EDİLMESİ

Bir işletmenin fiili durumu düzenli olarak açıkladığı finansal tablolardan belirlenir. Finansal tablolara bakılarak işletmenin finansal başarı durumları tespit edilir. Finansal başarısızlığa uğramış işletmelerin sayısındaki artış işletmelerin hem kendi kaynaklarının hem de ülke kaynaklarının iyi kullanılmadığının bir göstergesidir. Bu nedenle finansal başarısızlığın tahmin edilmesi önem arz eder. Bu çalışmada ilk olarak başarılı ve başarısız işletmeler belirlenerek istatistiki modeller kurulması için örnek, kestirim seti ve kontrol seti oluşturulmuştur. Daha sonra kontrol grubu ve veri seti kullanarak İMKB’de işlem gören 140 sanayi işletmesinin 2005-2008 yılları arasındaki finansal başarısızlıkları veri madenciliği ve diskriminant analizi modelleri ile tahmin ederek hangi yöntemin daha iyi sonuç verdiği tespit edilmiştir

Estimating financial failure of enterprises with data mining and discriminant analysis

Bir işletmenin fiili durumu düzenli olarak açıkladığı finansal tablolardan belirlenir. Finansal tablolara bakılarak işletmenin finansal başarı durumları tespit edilir. Finansal başarısızlığa uğramış işletmelerin sayısındaki artış işletmelerin hem kendi kaynaklarının hem de ülke kaynaklarının iyi kullanılmadığının bir göstergesidir. Bu nedenle finansal başarısızlığın tahmin edilmesi önem arz eder. Bu çalışmada ilk olarak başarılı ve başarısız işletmeler belirlenerek istatistiki modeller kurulması için örnek, kestirim seti ve kontrol seti oluşturulmuştur. Daha sonra kontrol grubu ve veri seti kullanarak İMKB’de işlem gören 140 sanayi işletmesinin 2005-2008 yılları arasındaki finansal başarısızlıkları veri madenciliği ve diskriminant analizi modelleri ile tahmin ederek hangi yöntemin daha iyi sonuç verdiği tespit edilmiştir.The actual status of the enterprises is determined by the regularly declared financial statements. By inspecting these financial statements, the financial success conditions of the enterprises are determined. The increase in the number of the financially failed enterprises is the indication of bad utilizing of both their own sources and the sources of the country. Therefore forecasting financial failures is important. In this study initially, example, forecasting set and control set are established to build statistical models by determining successful and unsuccessful enterprises. Then, the financial failures of 140 industrial companies listed in Istanbul Stock Exchange between 2005-2008 years are forecasted by using control set and data set. In these procedures data mining and discriminate analysis models were used and it is determined which model is giving better results

Estimating Financial Failure of Enterprises with Data Mining and Discriminant Analysis

Bir işletmenin fiili durumu düzenli olarak açıkladığı finansal tablolardan belirlenir. Finansal tablolara bakılarak işletmenin finansal başarı durumları tespit edilir. Finansal başarısızlığa uğramış işletmelerin sayısındaki artış işletmelerin hem kendi kaynaklarının hem de ülke kaynaklarının iyi kullanılmadığının bir göstergesidir. Bu nedenle finansal başarısızlığın tahmin edilmesi önem arz eder. Bu çalışmada ilk olarak başarılı ve başarısız işletmeler belirlenerek istatistiki modeller kurulması için örnek, kestirim seti ve kontrol seti oluşturulmuştur. Daha sonra kontrol grubu ve veri seti kullanarak İMKB’de işlem gören 140 sanayi işletmesinin 2005-2008 yılları arasındaki finansal başarısızlıkları veri madenciliği ve diskriminant analizi modelleri ile tahmin ederek hangi yöntemin daha iyi sonuç verdiği tespit edilmiştir.The actual status of the enterprises is determined by the regularly declared financial statements. By inspecting these financial statements, the financial success conditions of the enterprises are determined. The increase in the number of the financially failed enterprises is the indication of bad utilizing of both their own sources and the sources of the country. Therefore forecasting financial failures is important. In this study initially, example, forecasting set and control set are established to build statistical models by determining successful and unsuccessful enterprises. Then, the financial failures of 140 industrial companies listed in Istanbul Stock Exchange between 2005-2008 years are forecasted by using control set and data set. In these procedures data mining and discriminate analysis models were used and it is determined which model is giving better results

Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

In this study, an artificial neural network (ANN) model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010). Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin). The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm). Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 (~98%) was obtained with root mean square error (RMSE) of 0.852 MJ/m2, mean square error (MSE) of 0.725 MJ/m2, mean absolute bias error (MABE) 10.659MJ/m2, and mean absolute percentage error (MAPE) of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions

Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks

In this study, an artificial neural network (ANN) model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010). Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin). The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm). Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 (~98%) was obtained with root mean square error (RMSE) of 0.852 MJ/m2, mean square error (MSE) of 0.725 MJ/m2, mean absolute bias error (MABE) 10.659MJ/m2, and mean absolute percentage error (MAPE) of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions