Self-organizing maps could improve the classification of Spanish mutual funds.

In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from this alternative classification also attain better performances in terms of return/risk and include a smaller number of assets.Finance; Mutual funds; Clustering; Self-organizing map (SOM); Investment analysis;

Text Classification of installation Support Contract Topic Models for Category Management

Air Force Installation Contracting Agency manages nearly 18 percent of total Air Force spend, equating to approximately 57 billion dollars. To improve strategic sourcing, the organization is beginning to categorize installation-support spend and assign accountable portfolio managers to respective spend categories. A critical task in this new strategic environment includes the appropriate categorization of Air Force contracts into newly created, manageable spend categories. It has been recognized that current composite categories have the opportunity to be further distinguished into sub-categories leveraging text analytics on the contract descriptions. Furthermore, upon establishing newly constructed categories, future contracts must be classified into these newly constructed categories in order to be strategically managed. This research proposes a methodological framework for using Latent Dirichlet Allocation to sculpt categories from the natural distribution of contract topics, and assesses the appropriateness of supervised learning classification algorithms such as Support Vector Machines, Random Forests, and Weighted K-Nearest Neighbors models to classify future unseen contracts. The results suggest a significant improvement in modeled spend categories over the existing categories, facilitating more accurate classification of unseen contracts into their respective sub-categories

Data Mining Approach for Target Marketing SMEs in Nigeria

Presently, in addition to the lack of funds experienced by SMEs in Nigeria, the main challenges experienced by SMEs (Small and Medium Enterprises) in Nigeria has to do with them not being able to understand and the apply marketing concept. Also there are a lot of resources being wasted in marketing campaign which does not target anybody in particular. This study is therefore focused on finding the target market of SMEs using the K-means clustering technique in combination with sentiment analysis, also known as opinion mining. The study also aims at recommending the best medium to market these SMEs as discovered from the output of the analysis. The result of this research will give positive direction to improving the profit of small and medium business through target marketing. Also, this study is not trivial because it will help to reduce marketing cost through target marketing. The study also helps to discover the opinion of the populace on the small and medium business and the medium through which they can be targeted. This discovery will definitely form solid foundation for further marketing action and improve their profit

Rethinking the Role of Classification in Project Management Research

RÉSUMÉ : La vision universelle du projet, longtemps entretenue dans les cadres normatifs, cède peu à peu la place à une approche différenciée, où la notion de classification devient prépondérante. Toutefois, il semble bien que la communauté scientifique tarde à s’y intéresser; ce thème reste largement sous-étudié dans la littérature contemporaine dédiée à la gestion de projet. Il s’en suit une certaine confusion sémantique au sein de la communauté, touchant à la fois les postures philosophiques, mais aussi la terminologie et les processus qui y sont reliés. Cette thèse vise à établir la classification des projets comme sujet de recherche spécifique. À cette fin, et sur la base des contributions issues de plusieurs domaines scientifiques, ce projet doctoral propose une réflexion sur le processus de classification selon une perspective cognitiviste. Le premier article de cette thèse porte sur les différents processus cognitifs pouvant être sollicités par des acteurs organisationnels lors de la construction de systèmes de classification de projets. Le second article poursuit l’analyse en examinant empiriquement la compréhension que détiennent différents groupes vis-à-vis certaines catégories de projet. Ensemble, ces deux premiers articles ouvrent donc la "boîte noire" du processus cognitif de classification, et offrent une analyse sur la façon dont les classifications de projet sont établies par les chercheurs et les praticiens. Ce faisant, elles permettent de réduire la confusion et les multiples interprétations relatives aux catégories de projet, et qui ont longtemps constitué des freins à l’utilisation efficace de systèmes de classification, tant en recherche qu’en pratique. Dans le troisième article, la distinction entre les notions de classification et de typologie est analysée. On y soutient que la classification devrait constituer une condition préalable pour définir des types de projet et pour établir des théories "de portée intermédiaire (middle-range)"; on y défend également l’idée qu’une typologie de projet bien développée peut elle-même être considérée comme une théorie. En plus de proposer des retombées pratiques et concrètes pour les milieux de pratique, l’ensemble des articles de cette thèse permettent de jeter les bases d’un programme de recherche au sein de la communauté scientifique; ils contribuent également aux efforts actuels visant à consolider les bases théoriques de la gestion de projet. Mots clés: Classification du projet, catégorisation du projet, typologie des projets, taxonomie des projets, réussite du projet, théorie de portée intermédiaire, théorie de la gestion de projet----------ABSTRACT : Moving away from a universal view of projects, classification of projects has been recognized as an essential requirement for any investigation of project management. Yet classification as an independent topic of research has been understudied and undervalued in the project management literature. This issue has resulted in the development of semantic confusion among the project management researchers, with regard to philosophical stands, terminology, processes and implications of project classification. By rethinking the role of classification in a project management context, this dissertation aims to address this issue and establish project classification as an independent research topic. To that end and to keep up with recent advancements in classification research in other fields, this dissertation focuses more on evaluating the process of classification from a cognitive perspective. Accordingly, in the first article, different cognitive processes that individuals might apply to construct their project classification schemes are discussed. Delving further, the second article empirically examines the shared understanding of different groups vis-à-vis project categories. By opening the black box of the cognitive process of classification, the first two articles shed light on how and why different researchers or practitioners developed their project classification schemes. Thus, they reduce the ambiguities, inconsistencies and multiple interpretations of project categories, which have been identified as a main obstacle to the effective use of project classification systems in both research and practice. In the third article, the distinction between the definitions and implications of classification and typology is discussed. In particular, it is argued that classification should be a prerequisite to delimit project types and build up middle-range theories and that a well-developed project typology itself can be regarded as a theory. The collection of articles in this dissertation not only has important practical implications but, by laying the groundwork for establishing project classification as a research topic, fosters the theory development in the project management field. Keywords: Project classification, project categorization, project typology, project taxonomy, project success, middle-range theory, project management theor

Data Mining Approach for Target Marketing SMEs in Nigeria

Presently, in addition to the lack of funds experienced by SMEs in Nigeria, the main challenges experienced by SMEs (Small and Medium Enterprises) in Nigeria has to do with them not being able to understand and the apply marketing concept. Also there are a lot of resources being wasted inmarketing campaign which does not target anybody in particular. This study is therefore focused on finding the target market of SMEs using the K-means clustering technique in combination with sentiment analysis, also known as opinion mining. The study also aims at recommending the best medium to market these SMEs as discovered from the output of the analysis. The result of this research will give positive direction to improving the profit of small and medium business through target marketing. Also, this study is not trivial because it will help to reduce marketing cost through target marketing. The study also helps to discover the opinion of the populace on the small andmedium business and the medium through which they can be targeted. This discovery will definitely form solid foundation for further marketing action and improve their profit.Keywords: Data Mining, K-means Clustering, Target Marketing, Sentiment Analysis, SME, Opinion Mining

RFL-based customer segmentation using K-means algorithm

Customer segmentation has become crucial for the company’s survival and growth due to the rapid development of information technology (IT) and state-of-the-art databases that have facilitated the collection of customer data. Financial firms, particularly insurance companies, need to analyze these data using data mining techniques in order to identify the risk levels of their customer segments and revise the unproductive groups while retaining valuable ones. In this regard, firms have utilized clustering algorithms in conjunction with customer behavior-focused approaches, the most popular of which is RFM (recency, frequency, and monetary value). The shortcoming of the traditional RFM is that it provides a one-dimensional evaluation of customers that neglects the risk factor. Using data from 2586 insurance customers, we suggest a novel risk-adjusted RFM called RFL, where R stands for recency of policy renewal/purchase, F for frequency of policy renewal/purchase, and L for the loss ratio, which is the ratio of total incurred loss to the total earned premiums. Accordingly, customers are grouped based on the RFL variables employing the CRISP-DM and K-means clustering algorithm. In addition, further analyses, such as ANOVA as well as Duncan’s post hoc tests, are performed to ensure the quality of the results. According to the findings, the RFL performs better than the original RFM in customer differentiation, demonstrating the significant role of the risk factor in customer behavior evaluation and clustering in sectors that have to deal with customer risk

A Multidimensional and Visual Exploration Approach to Project Portfolio Management

Managing projects in an organization, especially a project-oriented organization, is a challenging task. Project data has a large volume and is complex to manage. It is different from managing a single project, because one needs to integrate and synthesize information from multiple projects and multiple perspectives for high-level strategic business decisions, such as aligning projects with business objectives, balancing investment and expected return, and allocating resources. Current methods and tools either do not well integrate multiple aspects or are not intuitive and easy to use for managers and executives. In this dissertation project, a multidimensional and visual exploration approach was designed and evaluated to provide a unique and intuitive option to support decision making in project portfolio management. The research followed a general design science research methodology involving phases of awareness of problem, suggestion, development, evaluation and conclusion. The approach was implemented into a software system using a prototyping method and was evaluated through user interviews. The evaluation result demonstrates the utility and ease-of-use of the approach, and confirms design objectives. The research brings a new perspective and provides a new decision support tool for project portfolio management. It also contributes to the design knowledge of visual exploration systems for business portfolio management by theorizing the system

Supplier Selection and Relationship Management: An Application of Machine Learning Techniques

Managing supply chains is an extremely challenging task due to globalization, short product life cycle, and recent advancements in information technology. These changes result in the increasing importance of managing the relationship with suppliers. However, the supplier selection literature mainly focuses on selecting suppliers based on previous performance, environmental and social criteria and ignores supplier relationship management. Moreover, although the explosion of data and the capabilities of machine learning techniques in handling dynamic and fast changing environment show promising results in customer relationship management, especially in customer lifetime value, this area has been untouched in the upstream side of supply chains. This research is an attempt to address this gap by proposing a framework to predict supplier future value, by incorporating the contract history data, relationship value, and supply network properties. The proposed model is empirically tested for suppliers of public works and government services Canada. Methodology wise, this thesis demonstrates the application of machine learning techniques for supplier selection and developing effective strategies for managing relationships. Practically, the proposed framework equips supply chain managers with a proactive and forward-looking approach for managing supplier relationship

Self-organizing maps could improve the classification of Spanish mutual funds

In this paper, we apply nonlinear techniques (Self-Organizing Maps, k-nearest neighbors and the k-means algorithm) to evaluate the official Spanish mutual funds classification. The methodology that we propose allows us to identify which mutual funds are misclassified in the sense that they have historical performances which do not conform to the investment objectives established in their official category. According to this, we conclude that, on average, over 40% of mutual funds could be misclassified. Then, we propose an alternative classification, based on a double-step methodology, and we find that it achieves a significantly lower rate of misclassifications. The portfolios obtained from this alternative classification also attain better performances in terms of return/risk and include a smaller number of assets.Publicad