Unsupervised learning algorithms applied to grouping problems

One of the tasks of great interest within process mining is the discovery of business process models, which consists of using an event log as input and producing a business process model by analyzing the data contained in the log and applying a process mining method, task and/or technique. The discovery allows the identification of the behaviors contained in the cases of the event log in order to detect possible deviations and/or validate that the business process is executed according to the business requirements. This paper presents an approach based on unsupervised learning techniques for the grouping of traces to generate simpler and more understandable models. The algorithms implemented for clustering are K-means, hierarchical agglomerative and density-based spatial clustering of applications with noise (DBSCAN)

Applying the agglomerative method in hierarchical clustering for the medium-sized companies listed on the Warsaw Stock Exchange

The purpose of this article is to use a hierarchical algorithm to reduce the number of companies in stock exchange portfolios, together with the identification of the most and least profitable groups of the companies. To prepare the research, the author decided to use a hierarchical clustering method to segment mWIG40 index entities. The conducted research contributed to the knowledge of the segments appearing on mWIG40 index and the profitability of the obtained clusters in the analyzed period. It was concluded that the hierarchical clustering method can divide the entities from mWIG40 index into six segments. The obtained groups differed from each other in terms of the analyzed features. Moreover, it was found that it was possible to identify more and less profitable segments in terms of the rate of return. What is more, only one segment was characterized by a higher rate of return than the benchmark. The findings can help investors to make better decisions during their investing process. In addition, the results can help companies to map their business in the market

A single currency for Asia? Evaluation and comparison using hierarchical and model-based cluster analysis

Today, there is increased speculation on the possibility of an Asian currency, as the region begins to show increased promise as a region of nascent economic activity. Any monetary integration scheme in East Asia would likely have to include both China and India though, so this paper attempts to assess the evolution of convergence among the East Asian countries, including China and India, according to the optimum currency area theory criteria, which is operationalized through the use of cluster analysis. In this paper we use both traditional "hierarchical" clustering as well as the more recently developed "model-based" clustering techniques and compare the outcome in each case. As the East Asian crisis of 1997-98 is likely to a¤ect the results, the exercise is done for pre-crisis, crisis, and post-crisis periods. The results reveal some structure among the countries, an increase in the degree of subregional homogeneity, and a robust relationship between Malaysia and Singapore

Building an IT Taxonomy with Co-occurrence Analysis, Hierarchical Clustering, and Multidimensional Scaling

Different information technologies (ITs) are related in complex ways. How can the relationships among a large number of ITs be described and analyzed in a representative, dynamic, and scalable way? In this study, we employed co-occurrence analysis to explore the relationships among 50 information technologies discussed in six magazines over ten years (1998-2007). Using hierarchical clustering and multidimensional scaling, we have found that the similarities of the technologies can be depicted in hierarchies and two-dimensional plots, and that similar technologies can be classified into meaningful categories. The results imply reasonable validity of our approach for understanding technology relationships and building an IT taxonomy. The methodology that we offer not only helps IT practitioners and researchers make sense of numerous technologies in the iField but also bridges two related but thus far largely separate research streams in iSchools - information management and IT management

Formal Identification of Right-Grained Services for Service-Oriented Modeling

Abstract. Identifying the right-grained services is important to lead the successful service orientation because it has a direct impact on two major goals: the composability of loosely-coupled services, and the reusability of individual services in different contexts. Although the concept of service orientation has been intensively debated in recent years, a unified methodic approach for identifying services has not yet been reached. In this paper, we suggest a formal approach to identify services at the right level of granularity from the business process model. Our approach uses the concept of graph clustering and provides a systematical approach by defining the cost metric as a measure of the interaction costs. To effectively extract service information from the business model, we take activities as the smallest units in service identification and cluster activities with high interaction cost into a task through hierarchical clustering algorithm, so as to reduce the coupling of remote tasks and to increase local task cohesion

A new perspective on the competitiveness of nations

The capability of firms to survive and to have a competitive advantage in global markets depends on, amongst other things, the efficiency of public institutions, the excellence of educational, health and communications infrastructures, as well as on the political and economic stability of their home country. The measurement of competitiveness and strategy development is thus an important issue for policy-makers. Despite many attempts to provide objectivity in the development of measures of national competitiveness, there are inherently subjective judgments that involve, for example, how data sets are aggregated and importance weights are applied. Generally, either equal weighting is assumed in calculating a final index, or subjective weights are specified. The same problem also occurs in the subjective assignment of countries to different clusters. Developed as such, the value of these type indices may be questioned by users. The aim of this paper is to explore methodological transparency as a viable solution to problems created by existing aggregated indices. For this purpose, a methodology composed of three steps is proposed. To start, a hierarchical clustering analysis is used to assign countries to appropriate clusters. In current methods, country clustering is generally based on GDP. However, we suggest that GDP alone is insufficient for purposes of country clustering. In the proposed methodology, 178 criteria are used for this purpose. Next, relationships between the criteria and classification of the countries are determined using artificial neural networks (ANNs). ANN provides an objective method for determining the attribute/criteria weights, which are, for the most part, subjectively specified in existing methods. Finally, in our third step, the countries of interest are ranked based on weights generated in the previous step. Beyond the ranking of countries, the proposed methodology can also be used to identify those attributes that a given country should focus on in order to improve its position relative to other countries, i.e., to transition from its current cluster to the next higher one

Managerial Segmentation of Service Offerings in Work Commuting, MTI Report WP 12-02

Methodology to efficiently segment markets for public transportation offerings has been introduced and exemplified in an application to an urban travel corridor in which high tech companies predominate. The principal objective has been to introduce and apply multivariate methodology to efficiently identify segments of work commuters and their demographic identifiers. A set of attributes in terms of which service offerings could be defined was derived from background studies and focus groups of work commuters in the county. Adaptive choice conjoint analysis was used to derive the importance weights of these attributes in available service offering to these commuters. A two-stage clustering procedure was then used to explore the grouping of individual’s subsets into homogeneous sub-groups of the sample. These subsets are commonly a basis for differentiation in service offerings that can increase total ridership in public transportation while approximating cost neutrality in service delivery. Recursive partitioning identified interactions between demographic predictors that significantly contributed to the discrimination of segments in demographics. Implementation of the results is discussed