Data mining framework based on rough set theory to improve location selection decisions: A case study of a restaurant chain

Location selection plays a crucial role in the retail and service industries. A comprehensive location selection model and appropriate analytical technique can improve the quality of location decisions, attracting more customers and substantially impacting market share and profitability. This study developed a data mining framework based on rough set theory (RST) to support location selection decisions. The proposed framework consists of four stages: (1) problem definition and data collection; (2) RST analysis; (3) rule validation; and (4) knowledge extraction and usage. An empirical study focused on a restaurant chain to demonstrate the validity of the proposed approach. Twenty location variables relevant to five location aspects were examined, and the results indicated that latent knowledge can be identified to support location selection decisions.     

Development and transformation of collaborative networks in events

This study examines the mechanisms by which and the reasons that leaders of non-profit organizations transform interorganizational collaborative networks through event attendance. Drawing from a two-wave survey, this study examines the interorganizational networks of 30 individuals who participated in both waves, along with the reported activities of 121 individuals who participated in the second wave of the survey. This mixed methods study found that events transformed participants’ weak ties into strong ties and promoted development of collaborative networks. Further, the study found that participants’ motivations for attending the event and following up with contacts afterward focused on sharing resources and deepening connections. Implications for understanding how events promote formation and transformation of collaborative networks are discussed.     

Tourism-based heritage conservation in Murshidabad: an appraisal

The article appraises the outcome of promoting tourism as a strategy for heritage conservation in the case of Murshidabad, an archetypal historic town in decline in India. A relational maturity-based framework has been used to examine the complementarities and conflicts between tourism and heritage conservation in the town. The prevailing nature of tourism conservation relationship in Murshidabad upholds the criticality characteristically observed in developing countries, where governments focus primarily on the revenue generating potential of built-heritage centric tourism ignoring the conservation attention necessary to retain authenticity and meaningful existence of historic structures. The overarching issues emerging from the current level of relational maturity in Murshidabad are identified with suggestions for possible policy implications of the study.     

水泥混凝土路面碎石化在老路改造施工中的应用

随着使用年限的增长和超载车辆的增加,多数水泥路面破坏严重而丧失整体的承载能力,且板块之间错台严重,致使其行车舒适性较差,急需维修改造以满足现代交通的需要。     

双面联采边角块段在矿井中的实践

本文阐述了双面联采边角块段在矿井中的实践。     

Assessing qualitative similarities between financial reporting frameworks using visualization and rules: COREP vs. pillar 3

Financial institutions are struggling with larger volume, more specific and greater frequency of regulatory reporting after the global financial crisis in 2008, especially those that need to report to multiple jurisdictions. To help to improve reporting efficiency, this paper aims to assess the existence of similarities between templates related to credit and counter party credit risk of COREP and Pillar 3 regulatory reporting frameworks by applying Correspondence Analysis and Association Rules Mining. Our results suggest a high degree of overlap between these reporting frameworks, more prominently the three business functions as Front office, Finance and Risk. These patterns can be used as guidance for financial institutions to reshape their reporting architecture.     

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.     

油气管道完整性评价的数据支持系统

开发了管道完整性评价的数据库系统，介绍了该软件系统的结构、模块、特点、主要功能和操作界面。     

资产规模、资本状况与商业银行资产组合行为——基于中国银行业面板数据的实证分析

本文立足于我国转型期内的经济金融特征,构造了一个银行资产组合行为的局部均衡模型。在对中国银行业按照资产规模和资本状况进行分类的基础上,本文的实证分析证明了模型的基本含义:由于银行间的异质性,紧缩性货币政策之后商业银行的资产组合行为体现出了截面效应。基于此,本文提出了确立以银行资产组合行为为基础的货币政策决策体系,加强货币政策与监管政策之间的协调等政策建议,以提高货币政策的有效性。     

Modelling bank customer behaviour using feature engineering and classification techniques

This study investigates customer behaviour and activity in the banking sector and uses various feature transformation techniques to convert the behavioural data into different data structures. Feature selection is then performed to generate feature subsets from the transformed datasets. Several classification methods used in the literature are applied to the original and transformed feature subsets. The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. This paper’s detailed systematic analysis of the modelling of bank customer behaviour can help banking institutions take the right steps to increase their customers’ activity.