Educational data mining and learning analytics : Clasificación de las matriculaciones de A.D.E. en la UOC

Trabajo de investigación que realiza un estudio clasificatorio de las asignaturas matriculadas en la carrera de Administración y Dirección de Empresas de la UOC en relación a su resultado. Se proponen diferentes métodos y modelos de comprensión del entorno en el que se realiza el estudio.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

Educational Data Mining: cerrando el círculo del proceso de aprendizaje en entornos virtuales

Development of methods to explore data from educational settings, to understand better the learning process.

Data mining and mall users profile

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

Efficiency of distinct data mining algorithms for classifying stress level in piglets from their vocalization

Among the challenges of pig farming in today's competitive market, there is factor of the product traceability that ensures, among many points, animal welfare. Vocalization is a valuable tool to identify situations of stress in pigs, and it can be used in welfare records for traceability. The objective of this work was to identify stress in piglets using vocalization, calling this stress on three levels: no stress, moderate stress, and acute stress. An experiment was conducted on a commercial farm in the municipality of Holambra, São Paulo State , where vocalizations of twenty piglets were recorded during the castration procedure, and separated into two groups: without anesthesia and local anesthesia with lidocaine base. For the recording of acoustic signals, a unidirectional microphone was connected to a digital recorder, in which signals were digitized at a frequency of 44,100 Hz. For evaluation of sound signals, Praat® software was used, and different data mining algorithms were applied using Weka® software. The selection of attributes improved model accuracy, and the best attribute selection was used by applying Wrapper method, while the best classification algorithms were the k-NN and Naive Bayes. According to the results, it was possible to classify the level of stress in pigs through their vocalization.

Using data mining to identify factors that influence the degree of leg injuries in broilers

Locomotor problems prevent the bird to move freely, jeopardizing the welfare and productivity, besides generating injuries on the legs of chickens. The objective of this study was to evaluate the influence of age, use of vitamin D, the asymmetry of limbs and gait score, the degree of leg injuries in broilers, using data mining. The analysis was performed on a data set obtained from a field experiment in which it was used two groups of birds with 30 birds each, a control group and one treated with vitamin D. It was evaluated the gait score, the asymmetry between the right and left toes, and the degree of leg injuries. The Weka ® software was used in data mining. In particular, C4.5 algorithm (also known as J48 in Weka environment) was used for the generation of a decision tree. The results showed that age is the factor that most influences the degree of leg injuries and that the data from assessments of gait score were not reliable to estimate leg weakness in broilers.

Data mining techniques for identification of spectrally homogeneous areas using NDVI temporal profiles of soybean crop

The aim of this study was to group temporal profiles of 10-day composites NDVI product by similarity, which was obtained by the SPOT Vegetation sensor, for municipalities with high soybean production in the state of Paraná, Brazil, in the 2005/2006 cropping season. Data mining is a valuable tool that allows extracting knowledge from a database, identifying valid, new, potentially useful and understandable patterns. Therefore, it was used the methods for clusters generation by means of the algorithms K-Means, MAXVER and DBSCAN, implemented in the WEKA software package. Clusters were created based on the average temporal profiles of NDVI of the 277 municipalities with high soybean production in the state and the best results were found with the K-Means algorithm, grouping the municipalities into six clusters, considering the period from the beginning of October until the end of March, which is equivalent to the crop vegetative cycle. Half of the generated clusters presented spectro-temporal pattern, a characteristic of soybeans and were mostly under the soybean belt in the state of Paraná, which shows good results that were obtained with the proposed methodology as for identification of homogeneous areas. These results will be useful for the creation of regional soybean "masks" to estimate the planted area for this crop.

Vocalization data mining for estimating swine stress conditions

This study aimed to identify differences in swine vocalization pattern according to animal gender and different stress conditions. A total of 150 barrow males and 150 females (Dalland® genetic strain), aged 100 days, were used in the experiment. Pigs were exposed to different stressful situations: thirst (no access to water), hunger (no access to food), and thermal stress (THI exceeding 74). For the control treatment, animals were kept under a comfort situation (animals with full access to food and water, with environmental THI lower than 70). Acoustic signals were recorded every 30 minutes, totaling six samples for each stress situation. Afterwards, the audios were analyzed by Praat® 5.1.19 software, generating a sound spectrum. For determination of stress conditions, data were processed by WEKA® 3.5 software, using the decision tree algorithm C4.5, known as J48 in the software environment, considering cross-validation with samples of 10% (10-fold cross-validation). According to the Decision Tree, the acoustic most important attribute for the classification of stress conditions was sound Intensity (root node). It was not possible to identify, using the tested attributes, the animal gender by vocal register. A decision tree was generated for recognition of situations of swine hunger, thirst, and heat stress from records of sound intensity, Pitch frequency, and Formant 1.

Business Intelligence -ohjelmistot päätöksenteon tukena

Työn tavoitteena on tutkia Business Intelligence -ohjelmistojen käyttöä päätöksenteon tukena. Lisäksi tutkitaan näiden ohjelmistojen merkitystä yrityksille. Työssä tarkastellaan myös mahdollisia tulevaisuuden näkymiä. Työ on kirjallisuustyö, joka pohjautuu lähdeaineistoon. Työn tuloksena on huomattu, kuinka tärkeitä Business Intelligence -ohjelmistot ovat yritysten päätöksenteossa. Suuren tietomäärän vuoksi on tärkeää, että yrityksellä on työkalu, jonka avulla kaikki merkityksellinen tieto saadaan välitettyä päätöksentekijöille. Business Intelligence -ohjelmistot tuottavat monenlaisia analyyseja, joiden avulla voidaan tehdä onnistuneita päätöksiä. Mitä tarkempia analyyseja tehdään, sitä enemmän voidaan myös saavuttaa kilpailuetua. Business Intelligence -ohjelmistojen avulla yrityksillä on mahdollisuus saavuttaa monia erilaisia hyötyjä. Hyötyjen mittaaminen on kuitenkin haastavaa, koska osa hyödyistä on aineettomia. Hyötyjen ja liikearvon mittaamiseen on kehitetty mittareita, joiden avulla on tarkoitus pystyä perustelemaan Business Intelligence -ohjelmistoihin investointia. Tulevaisuudessa Business Intelligence -ohjelmistojen merkitys yrityksille kasvaa. Yritysten muuttuvia tarpeita varten kehitetään uudenlaisia Business Intelligence -sovelluksia. Teknologia ja ohjelmistojen innovatiivinen käyttö muokkaavat BI-ohjelmistoja tehokkaammiksi. Jatkuva uusien sovellusten kehittäminen luo myös haasteita ennen niiden laajempaa käyttöönottoa.

”Tuntuu, et nää on vielä niin lapsen kengissä tämmöset HR:n luvut…”: Työhyvinvoinnin panostusten ja kustannusten Business Intelligence

Työhyvinvointi saa suomalaisessa työelämässä paljon huomiota juuri tällä hetkellä ja se on tunnis-tettu yrityksen kannalta hyödylliseksi alueeksi, johon kannattaa panostaa. Matalat työhyvinvointiin liittyvät kustannukset, organisaation parempi kilpailukyky ja maine sekä korkea henkilöstön tuotta-vuus ovat esimerkkejä työhyvinvoinnin kautta saavutettavista hyödyistä. Yritys voi parantaa hen-kilöstön työhyvinvointia panostamalla siihen ja tämän pitäisi näkyä suoraan myös työhyvinvointiin liittyvissä tunnusluvuissa, kuten sairauspoissaolo-, työkyvyttömyyseläke- ja vaihtuvuuskustannuk-sissa. Yritykset voivat hyödyntää työhyvinvoinnin panostusten ja kustannusten tunnuslukuja, jos niitä hallitaan onnistuneesti. Tällöin pitää tietää, mistä kustannukset koostuvat ja ymmärtää, millä pa-nostuksilla niihin voidaan vaikuttaa. Business Intelligence on termi, jolla tarkoitetaan kaikkea sel-laista organisaation tiedon hallintaa, jota tarvitaan liiketoiminnan johtamisessa. Sen tavoitteena on tukea organisaatiossa tapahtuvaa päätöksentekoa. Business Intelligence voidaan kuvata prosessina, jossa organisaatio kerää, yhdistelee ja analysoi tietoa eri tietolähteistä. Analysoinnin jälkeen yrityk-sellä on käytössään uutta tietämystä, jota voidaan hyödyntää päätöksenteossa. Toimivien Business Intelligence ratkaisujen ansiosta yrityksen päätöksenteon varmuus ja vaikuttavuus kasvaa. Tämän tutkimuksen tarkoituksena on selvittää, millainen on tehokas työhyvinvoinnin panostusten ja kustannusten hallintaan liittyvä Business Intelligence prosessi. Tehokas prosessi auttaa paranta-maan työhyvinvointiin tehtävien panostusten oikeellisuutta ja nostaa organisaation ymmärrystä omasta toiminnastaan. Tutkimusongelmaan on haettu vastauksia laadullisten tutkimusmenetelmien avulla haastattelemalla työhyvinvoinnin panostusten ja kustannusten tunnuslukuja seuraavia yrityk-siä. Tutkimuksen tulokset on analysoitu käyttämällä hyväksi kirjallisuuteen perustuvaa teema-ana-lyysia, jossa työhyvinvointiin ja Business Intelligenceen liittyvää teoriaa verrataan haastatteluista saatuihin vastauksiin. Tutkimuksen johtopäätöksenä tehokas työhyvinvoinnin panostusten ja kustannusten Business Intelligence prosessi voidaan kuvata perinteisten BI:n tasojen kautta. Työhyvinvoinnin panostusten ja kustannusten tunnuslukujen kerääminen onnistuu eri osapuolten välisen yhteistyön avulla. Tun-nusluvuista voi luoda merkityksellistä tietämystä, kun niistä saa tarkkaa tietoa ja niiden rinnalle tuodaan laadullista aineistoa työntekijöiden kokemuksista. Prosessia tukee se, jos teknologian kautta päätöksentekijä pystyy hankkimaan tarvittavat tiedot itse. Tulevaisuudessa olisi tärkeää, että saa-daan enemmän tietoa eri toimenpiteiden vaikuttavuudesta tunnuslukuihin, jotta päätöksiä voidaan tehdä luotettavamman tietämyksen varassa.