Highlights? We proposed a novel fuzzy clustering method for geo-demographic analysis. ? We showed some theorems and properties of this method. ? We validated it through extensive experimentation using the real-world datasets. ? Results showed that our method outperforms some of the best-known approaches. Geo-Demographic Analysis (GDA) is an important tool to explore the underlying rules that regulate our world, and therefore, it has been widely applied to the development of effective socio-economic policies through the analysis of data generated from Geographic Information Systems (GIS). In GDA applications, clustering plays a major role however, the current state-of-the-art algorithms, namely the Fuzzy Geographically Weighted Clustering (FGWC), have demonstrated several limitations both in terms of speed and in terms of quality of the achieved results. Accordingly, in this paper, we propose a novel clustering algorithm for GDA application, based on recent results regarding intuitionistic fuzzy sets and the possibilistic fuzzy C-means, that aims at overcoming some of the limitations of the existing methods.

A novel intuitionistic fuzzy clustering method for geo-demographic analysis

Wind turbine power output very short-term forecast: A comparative study of data clustering techniques in a PSO-ANFIS model

An enhanced sample average approximation method for stochastic optimization

Related to the recent studies of the last decade, which concentrate on the banking performance, researchers have employed the linear models profoundly; however, this trend is about to change due to the prediction performance of ML techniques as well as ANN, DVM, Decision Trees and their ensembles. Considering the analysis concentrated on Turkish banking profit analysis, just the linear models with panel data or multiple regression analysis were applied. In the literature, to the best our knowledge there is no other study that uses more developed models like ensemble models and also there is no significant analysis considering the sub-bank group like IaDB in Turkey.From this two important point of view, this research would fill the gap which caused by using not sophisticated models and just concentrating on the whole banking group with different characteristics.We conduct a comparative assessment of the performance of three bagging ensemble models, on prediction of Turkish IaDB profitability. To the best of our knowledge, ensemble methods have not been applied extensively in banks profitability analyses especially in IaDB profitability predictions. The purpose of this study is to find the determinants of the profits for the Development and Investment Banks (IaDB) in Turkey. In Turkish Banking System, the main financial source of the banks is the deposits, which constitute almost%60 of the balance sheet. As being a sub-group of the banking system, IaDB are not allowed to accept deposits in Turkey, which changes the total structure of the profitability compared to other banks. Till today, none of the relevant research was concentrated on the profit structure of the IaDB neither in Turkey nor in any other countries. Such research would fill that unexpectedly disregarded yet highly important gap.Therefore, to address this gap, quarterly financial data (10 balance sheet ratios) of 13 banks in the period of 2002Q4-2014Q3 were utilized. As a profit measurement among all other available measures, Return on Equity was chosen as dependent variable as it was the most used one as well as many other researcher have preferred as well. This study investigates the potential usage of bagging (Bag), which is one of the most popular ensemble learning methods, in building ensemble models, is used to predict the determinants of Turkish IaDB profitability. Three well-known tree-based machine learning (ML) models (i.e., Decision Stump (DStump), Random Tree (RTree), Reduced Error Pruning Tree (REPTree)) are deployed as base learner. This empirical study indicates that bagging ensemble models (i.e., Bag-DStump, Bag-RTree, Bag-MLP and Bag-REPTree) are superior to their base learners and could improve the prediction accuracy of individual ML models (i.e., DStump, RTree, REPTree).

Bagging ensemble models for bank profitability

Purpose




The purpose of this paper is to present a new public bread factory location selection for Istanbul Metropolitan Municipality (IMM)




Design/methodology/approach




A two-stage methodology is proposed to determine the location for the public bread factory facility This framework is based on both geographic information systems (GIS) and multi-criteria decision-making (MCDM) techniques The first stage of the methodology aims to decrease the number of possible alternative locations to simplify the selection activity by applying GIS; the second stage utilises interval type-2 fuzzy MCDM approach to exactly determine the public bread factory site location




Findings




In this study, the authors present weighted normalised-based interval type-2 hesitant fuzzy and interval type-2 hesitant fuzzy sets (IT2HFSs)-based compressed proportional assessment (COPRAS) methods to overcome facility location selection problem for a fourth public bread factory in Istanbul




Practical implications




The results show that the proposed approach is practical and can be employed by the bakery industry




Originality/value




In this study, the authors present a two-stage methodology for public bread factory site selection In the first stage, the number of alternatives is reduced by the GIS In the second stage, an interval type-2 fuzzy set is implemented for the evaluation of public bakery factory site alternatives A new integrated approach based on COPRAS method and weighted normalised with IT2HFSs is proposed

A GIS-based interval type-2 fuzzy set for public bread factory site selection

In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply centres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which optimizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer's real-world data in Turkey are reported. Numerical results show that the proposed approach performs better than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.

Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

Capacity utilization rate is one of the most important indicators of the efficiency of the manufacturing industry, andtherefore of the return of the investments made. Estimation of these rates accurately renders it possible to make importanteconomic decisions such as taking sectorial investment decisions, defining the optimal distribution of sectorial credits,determining non-competitive sectors, making development plans and developing unemployment policies. In this study, weestimated the capacity utilization rates of 21 sub-sectors of the Turkish manufacturing industry using support vectormachines and compared the results with the results obtained from the methods of artificial neural networks and vectorauto-regression. This study is the first in the literature in that it was carried out using this method.

/pdf/estimation-of-the-manufacturing-industry-sub-sectors-2b76ua39bs.pdf

Estimation of the manufacturing industry sub-sectors' capacity utilization rates using support vector machines

Copula is a distribution function on the unit hypercube with uniform margins. The margin is directly related to the stochastic behaviour of one variable, while joint distribution function covers the holistic character of more. In multivariate (and particularly bivariate) analysis, using copulas is an elegant way to solve the missing information problem between joint distribution function and the total of the margins. Hereby, the intention of this paper is twofold. Firstly, the paper intends to emphasize the advantages of copulas in practice. In order to encourage potential researchers to diversify their subject of work with these functions, authors give the essential introductory details for a clear understanding of copulas associated with their basic mathematical and statistical preliminaries. Secondly, the study exemplifies the practical usage of copulas in statistical process control area. In this context, process parameters are estimated in order to calculate the control limits of a typical Shewhart type control chart. Parameter estimation is performed by Maximum Likelihood Estimation (MLE) for the bivariate Clayton copula in univariate AR (1) time series with several different levels of high dependence. Since monitoring autocorrelated data in control charts is known as being one of the main causes of producing tighter control limits than required, false alarm rate may be increased and accordingly, the performance of control charts may be dramatically decreased. This study shows that copulas may alternatively be used for getting the same or little wider acceptable region between upper and lower limits. This recognition of the properness of copulas may help to decrease some of the negative effects of dependent data being monitored on charts for further studies.

On Copula Based Serial Dependence in Statistical Process Control

Document clustering, which involves concepts from the fields of information retrieval, automatic topic extraction, natural language processing, and machine learning, is one of the most popular research areas in data mining. Due to the large amount of information in electronic form, fast and high-quality cluster analysis plays an important role in helping users to effectively navigate, summarize and organise this information for useful data. There are a number of techniques in the literature, which efficiently provide solutions for document clustering. However, during the last decade, researchers started to use metaheuristic algorithms for the document clustering problem because of the limitations of the existing traditional clustering algorithms. In this chapter, the authors will give a brief review of various research papers that present the area of document or text clustering approaches with different metaheuristic algorithms.

A Brief Review of Metaheuristics for Document or Text Clustering

The multi-level warehouse layout problem with different z-axis storage spaces is investigated. In this study, a design methodology for a multiple-level warehouse layout (MLWL) is developed in order to minimize the total material handling costs by considering different number of the storage areas allocated to different groups along the z-axis under physical constraints. The proposed mathematical model is NP-hard. Boundary conditions are often used in particle swarm optimization (PSO) in order to keep the particles as much as possible in the allowable solution spaces. Moreover, two new boundary conditions are proposed for keeping the particles in the allowable solution spaces. Besides, a proposed assignment algorithm for particles’ initial values is used to overcome the problem of the time lost while particles are searching inappropriate solutions in allowable solution spaces

/pdf/design-methodology-for-a-multiple-level-warehouse-layout-2s1jrhzii4.pdf

Alp Baray

Papers

Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

Estimation of the manufacturing industry sub-sectors' capacity utilization rates using support vector machines

On Copula Based Serial Dependence in Statistical Process Control

A Brief Review of Metaheuristics for Document or Text Clustering

Design methodology for a multiple-level warehouse layout based on particle swarm optimization algorithm