Many investors use different types of data methods before making a decision, regardless of whether it is long or short term. The choice of which analysis method is generally determined by risk, removal of bias and the cost. One method that has been investigated is the use of machine lerning in data analysis. The advantage of machine lernig is that the method successfully handles comples, non-linear and non-stationary problems. In this essay, it will be investigated whether unattended machine learning, which uses the K-meaning method, which is a method that has not been investigated to any great extent either in practice or in theory to create a beneficial portfolio. The data used for the k-meaning method was historical data from the Swedish stock market between 1 January 2018 and 2 November 2020. The k-meaning analysis consists of the return of all shares included within OMX30 and the average deviation, which created a cluster of 11 shares that could generate a relatively high return compared to the remaining shares. To analyze whether the generated cluster were acceptable, an analysis of the sharpe-ratio and downward risk was preformed, which showed that the portfolio had a good risk-adjusted returnbut a worse result on downward risk.

Increasing concern about global climate change has led to a growing interest in energy usage and water consumption. It is well known that changes in consumption habits lead to more efficient use of energy and water sources. Nowadays, globalization, environmental concerns, and the shortage of resources have led to an increase of stakeholder pressure on companies to expand their focus to sustainability. Also, the high impact that the savings can have in the financial status of the company. It is encouraging the headboards to study and improve the ways water and energy are being used within the processes. Significant economic savings and benefits for the environment could be achieved with slight changes in the company. As an overview, this project starts with the extraction of data from a platform for energy management in an industrial company. Then, it goes through the understanding of the energy and water usage data set. Later, a methodology to handle and process the data will be set. It is intending to extract relevant information using clustering. The idea is to compare the usage profiles between different factories, using key performance indicators and reducing the initial data set. Once the benchmarking is performed, some critical parameters will be selected to support the decision-making process related to investments to reduce the energy usage and water consumption in a specific location. Finally, the case of study will be implemented with the measurements from Alfa Laval. We will study how, from daily measurements with a very low investment and using the proper algorithms and methodologies, the main behaviours and features in an industrial location can be extracted from the utilization data. These characteristics can be used to develop strategies or productions schemes based on the interests of the energy manager and the company.

Social networks are a popular form of communication. They are also used by universities in order to simplify information providing and addressing candidates for study. Foreign study stays are also a popular form of education. Students, however, encounter a number of obstacles. The results of this work can help universities make their social network communication more efficient and better support foreign studies. In this work, the data from Facebook related to Czech universities and the Erasmus program questionnaire data were analyzed in order to find useful knowledge. The main emphasis was on textual content of communication. The statistical and machine learning methods, including mostly feature selection, topic modeling and clustering were used. The results reveal interesting and popular topics discussed on Czech universities social networks. The main problems of students related to their foreign studies were identified too and some of them were compared for countries and universities.

In today's society, companies are trying to find ways to utilize all the data they have, which considers valuable information and insights to make better decisions. This includes data used to keeping track of timber that flows between forest and industry. The growth of Artificial Intelligence (AI) and Machine Learning (ML) has enabled the development of ML modes to automate the measurements of timber on timber trucks, based on images. However, to improve the results there is a need to be able to get information from unlabeled images in order to decide weather and lighting conditions. The objective of this study is to perform an extensive for classifying unlabeled images in the categories, daylight, darkness, and snow on the load. A comparative study between partitional clustering and competitive learning is conducted to investigate which method gives the best results in terms of different clustering performance metrics. It also examines how dimensionality reduction affects the outcome. The algorithms K-means and Kohonen Self-Organizing Map (SOM) are selected for the clustering. Each model is investigated according to the number of clusters, size of dataset, clustering time, clustering performance, and manual samples from each cluster. The results indicate a noticeable clustering performance discrepancy between the algorithms concerning the number of clusters, dataset size, and manual samples. The use of dimensionality reduction led to shorter clustering time but slightly worse clustering performance. The evaluation results further show that the clustering time of Kohonen SOM is significantly higher than that of K-means.

This work presents the topic of data mining on the web. It is focused on clustering. The aim of this project was to study the field of clustering and to implement clustering through the k-means algorithm. Then, the algorithm was tested on a dataset of text documents and on data extracted from web. This clustering method was implemented by means of Java technologies.

No description available.

博士(工学) / Doctor of Philosophy in Engineering / 同志社大学 / Doshisha University

The Customer Success department at Visma handles about 200 000 customer chats each year, the chat dialogues are stored and contain both questions and answers. In order to get an idea of what customers ask about, the Customer Success department has to read a random sample of the chat dialogues manually. This thesis develops and investigates an analysis tool for the chat data, using the approach of clustering and summarization. The approach aims to decrease the time spent and increase the quality of the analysis. Models for clustering (K-means, DBSCAN and HDBSCAN) and extractive summarization (K-means, LSA and TextRank) are compared. Each algorithm is combined with three different text representations (TFIDF, S-BERT and FastText) to create models for evaluation. These models are evaluated against a test set, created for the purpose of this thesis. Silhouette Index and Adjusted Rand Index are used to evaluate the clustering models. ROUGE measure together with a qualitative evaluation are used to evaluate the extractive summarization models. In addition to this, the best clustering model is further evaluated to understand how different data sizes impact performance. TFIDF Unigram together with HDBSCAN or K-means obtained the best results for clustering, whereas FastText together with TextRank obtained the best results for extractive summarization. This thesis applies known models on a textual domain of customer chat dialogues, something that, to our knowledge, has previously not been done in literature.

[pt] Os sistemas de transmissão cumprem uma função vital para o bom desempenho dos mercados de energia elétrica. A precificação do seu uso afeta diretamente a remuneração das empresas concessionárias e os custos dos participantes do mercado. No Brasil, os usuários do sistema interligado nacional (SIN) devem pagar pela disponibilização dos equipamentos que compõem a rede para as transmissoras detentoras destes ativos de forma proporcional ao seu uso. Assim, a agência reguladora brasileira (ANEEL) estabeleceu as tarifas de uso do sistema de transmissão (TUST), que são calculadas anualmente por barra via metodologia nodal. Tais tarifas são compostas por duas parcelas: locacional e selo. A parcela locacional reflete o uso efetivo da rede por cada agente participante, medindo o impacto da injeção de potência marginal de uma barra nos equipamentos do sistema. A parcela selo consiste num valor constante que garantirá a remuneração da porção não utilizada da rede. Em geral, a proximidade elétrica das barras do sistema implica valores tarifários similares. Esta Dissertação de Mestrado propõe uma nova metodologia a ser incorporada no cálculo da TUST, considerando a divisão do SIN em zonas tarifárias de transmissão (ZTT). Desta forma, cada ZTT apresentará uma única tarifa a ser aplicada aos seus participantes, que corresponderá à média ponderada das tarifas finais calculadas via metodologia nodal. Para a identificação das ZTT, são aplicadas técnicas de agrupamento k-Means e espectral nos sistemas IEEE-RTS e SIN. Nesta dissertação, avalia-se também o uso de modelos matemáticos para definir o número ideal de ZTT a ser considerado. São realizadas diversas análises de sensibilidade relativas a mudanças de despacho, alterações de topologia e evolução do sistema ao longo dos anos. Os resultados correspondentes são então extensivamente discutidos. / [en] Transmission systems play a vital role in the good performance of the electrical energy markets. The pricing of its use directly affects the budget of concessionary companies and the costs of market participants. In Brazil, users of the national interconnected system (NIS) must pay for the equipment availability that makes up the network to the transmission companies that own these assets in proportion to their use. Thus, the Brazilian regulatory agency (ANEEL) established the tariffs for transmission system usage (TTSU), which are calculated annually by bus using the nodal methodology. Such tariffs are made up of two installments: locational and postage stamp. The locational portion reflects the effective use of the grid by each participating agent, measuring the impact of the marginal power injection at a bus on the system equipment. The stamp portion consists of a constant amount that will guarantee the remuneration of the unused portion of the network. In general, the electrical proximity of the system buses leads to similar tariff values. This dissertation proposes a new methodology to be incorporated into the TTSU calculation, considering the division of the NIS into transmission tariff zones (TTZ). In this way, each TTZ will present a single tariff to be applied to its participants, which will correspond to the weighted average of the final tariffs calculated via the nodal methodology. For the identification of the TTZ, k-Means and Spectral clustering techniques are applied to the IEEE-RTS and SIN systems. In this dissertation, the use of mathematical models is also assessed to define the ideal number of TTZ to be considered. Various sensitivity analyses are carried out regarding changes in dispatch, grid topology and expansion of the system over the years. The corresponding results are deeply discussed.

With the help of new technology it has become much easier to apply for a job. Reaching out to a larger audience also results in a lot of more applications to consider when hiring for a new position. This has resulted in that many big companies uses statistical learning methods as a tool in the first step of the recruiting process. Smaller companies that do not have access to the same amount of historical and big data sets do not have the same opportunities to digitalise their recruitment process. Using topological data analysis, this thesis explore how clustering methods can be used on smaller data sets in the early stages of the recruitment process. It also studies how the level of abstraction in data representation affects the results. The methods seem to perform well on higher level job announcements but struggles on basic level positions. It also shows that the representation of candidates and jobs has a huge impact on the results. / Ny teknologi har förenklat processen för att söka arbete. Detta har resulterat i att företag får tusentals ansökningar som de måste ta hänsyn till. För att förenkla och påskynda rekryteringsprocessen har många stora företag börjat använda sig av maskininlärningsmetoder. Mindre företag, till exempel start-ups, har inte samma möjligheter för att digitalisera deras rekrytering. De har oftast inte tillgång till stora mängder historisk ansökningsdata. Den här uppsatsen undersöker därför med hjälp av topologisk dataanalys hur klustermetoder kan användas i rekrytering på mindre datauppsättningar. Den analyserar också hur abstraktionsnivån på datan påverkar resultaten. Metoderna visar sig fungera bra för jobbpositioner av högre nivå men har problem med jobb på en lägre nivå. Det visar sig också att valet av representation av kandidater och jobb har en stor inverkan på resultaten.