Visual customer segmentation and behavior analysis : a SOM-based approach

Ett ämne som väckt intresse både inom industrin och forskningen är hantering av kundförhållanden (CRM, eng. Customer Relationship Management), dvs. en kundorienterad affärsstrategi där företagen från att ha varit produktorienterade väljer att bli mera kundcentrerade. Numera kan kundernas beteende och aktiviteter lätt registreras och sparas med hjälp av integrerade affärssystem (ERP, eng. Enterprise Resource Planning) och datalager (DW, eng. Data Warehousing). Kunder med olika preferenser och köpbeteende skapar sin egen ”signatur” i synnerhet via användningen av kundkort, vilket möjliggör mångsidig modellering av kundernas köpbeteende. För att få en översikt av kundernas köpbeteende och deras lönsamhet, används ofta kundsegmentering som en metod för att indela kunderna i grupper utgående från deras likheter. De mest använda metoderna för kundsegmentering är analytiska modeller konstruerade för en viss tidsperiod. Dessa modeller beaktar inte att kundernas beteende kan förändras med tiden. I föreliggande avhandling skapas en holistisk översikt av kundernas karaktär och köpbeteende som utöver de konventionella segmenteringsmodellerna även beaktar dynamiken i köpbeteendet. Dynamiken i en kundsegmenteringsmodell innefattar förändringar i segmentens struktur och innehåll, samt förändringen av individuella kunders tillhörighet i ett segment (s.k migrationsanalyser). Vardera förändringen modelleras, analyseras och exemplifieras med visuella datautvinningstekniker, främst med självorganiserande kartor (SOM, eng. Self-Organizing Maps) och självorganiserande tidskartor (SOTM), en vidareutveckling av SOM. Visualiseringen anteciperas underlätta tolkningen av identifierade mönster och göra processen med kunskapsöverföring mellan den som gör analysen och beslutsfattaren smidigare. Asiakkuudenhallinta (CRM) eli organisaation muuttaminen tuotepainotteisesta asiakaskeskeiseksi on herättänyt mielenkiintoa niin yliopisto- kuin yritysmaailmassakin. Asiakkaiden käyttäytymistä ja toimintaa pystytään nykyään helposti tallentamaan ja varastoimaan toiminnanohjausjärjestelmien ja tietovarastojen avulla; asiakkaat jättävät jatkuvasti piirteistään ja ostokäyttäytymisestään kertovia tietojälkiä, joita voidaan analysoida. On tavallista, että asiakkaat poikkeavat toisistaan eri tavoin, ja heidän mieltymyksensä kuten myös ostokäyttäytymisensä saattavat olla hyvinkin erilaisia. Asiakaskäyttäytymisen monimuotoisuuteen ja tuottavuuteen paneuduttaessa käytetäänkin laajalti asiakassegmentointia eli asiakkaiden jakamista ryhmiin samankaltaisuuden perusteella. Perinteiset asiakassegmentoinnin ratkaisut ovat usein yksittäisiä analyyttisia malleja, jotka on tehty tietyn aikajakson perusteella. Tämän vuoksi ne monesti jättävät huomioimatta sen, että asiakkaiden käyttäytyminen saattaa ajan kuluessa muuttua. Tässä väitöskirjassa pyritäänkin tarjoamaan holistinen kuva asiakkaiden ominaisuuksista ja ostokäyttäytymisestä tarkastelemalla kahta muutosvoimaa tiettyyn aikarajaukseen perustuvien perinteisten segmentointimallien lisäksi. Nämä kaksi asiakassegmentointimallin dynamiikkaa ovat muutokset segmenttien rakenteessa ja muutokset yksittäisten asiakkaiden kuulumisessa ryhmään. Ensimmäistä dynamiikkaa lähestytään ajallisen asiakassegmentoinnin avulla, jossa visualisoidaan ajan kuluessa tapahtuvat muutokset segmenttien rakenteissa ja profiileissa. Toista dynamiikkaa taas lähestytään käyttäen nk. segmenttisiirtymien analyysia, jossa visuaalisin keinoin tunnistetaan samantyyppisesti segmentistä toiseen vaihtavat asiakkaat. Visualisoinnin tehtävänä on tukea havaittujen kaavojen tulkitsemista sekä helpottaa tiedonsiirtoa analysoijan ja päättäjien välillä. Visuaalisia tiedonlouhintamenetelmiä, kuten itseorganisoivia karttoja ja niiden laajennuksia, käytetään osoittamaan näiden menetelmien hyödyllisyys sekä asiakkuudenhallinnassa yleisesti että erityisesti asiakassegmentoinnissa.

Libyan breast cancer: Health services and biology. Diagnosis delay and prognostic value of DNA pploidy, S-phase fraction, and Ki-67 and Bcl-2 immunohistochemistry

The aim of this study was to investigate the diagnosis delay and its impact on the stage of disease. The study also evaluated a nuclear DNA content, immunohistochemical expression of Ki-67 and bcl-2, and the correlation of these biological features with the clinicopathological features and patient outcome. 200 Libyan women, diagnosed during 2008–2009 were interviewed about the period from the first symptoms to the final histological diagnosis of breast cancer. Also retrospective preclinical and clinical data were collected from medical records on a form (questionnaire) in association with the interview. Tumor material of the patients was collected and nuclear DNA content analysed using DNA image cytometry. The expression of Ki-67 and bcl-2 were assessed using immunohistochemistry (IHC). The studies described in this thesis show that the median of diagnosis time for women with breast cancer was 7.5 months and 56% of patients were diagnosed within a period longer than 6 months. Inappropriate reassurance that the lump was benign was an important reason for prolongation of the diagnosis time. Diagnosis delay was also associated with initial breast symptom(s) that did not include a lump, old age, illiteracy, and history of benign fibrocystic disease. The patients who showed diagnosis delay had bigger tumour size (p<0.0001), positive lymph nodes (p<0.0001), and high incidence of late clinical stages (p<0.0001). Biologically, 82.7% of tumors were aneuploid and 17.3% were diploid. The median SPF of tumors was 11% while the median positivity of Ki-67 was 27.5%. High Ki-67 expression was found in 76% of patients, and high SPF values in 56% of patients. Positive bcl-2 expression was found in 62.4% of tumors. 72.2% of the bcl-2 positive samples were ER-positive. Patients who had tumor with DNA aneuploidy, high proliferative activity and negative bcl-2 expression were associated with a high grade of malignancy and short survival. The SPF value is useful cell proliferation marker in assessing prognosis, and the decision cut point of 11% for SPF in the Libyan material was clearly significant (p<0.0001). Bcl-2 is a powerful prognosticator and an independent predictor of breast cancer outcome in the Libyan material (p<0.0001). Libyan breast cancer was investigated in these studies from two different aspects: health services and biology. The results show that diagnosis delay is a very serious problem in Libya and is associated with complex interactions between many factors leading to advanced stages, and potentially to high mortality. Cytometric DNA variables, proliferative markers (Ki-67 and SPF), and oncoprotein bcl-2 negativity reflect the aggressive behavior of Libyan breast cancer and could be used with traditional factors to predict the outcome of individual patients, and to select appropriate therapy.

Benzodiazepines and Cognitive Functioning in Older Adults. With Emphasis on Long-Term Use and Withdrawal

Benzodiazepines (BZD) and benzodiazepine related drugs (RD) are the most commonly used psychotropics among the aged. The use of other psychotropics taken concomitantly with BZD/ RD or their cognitive effects with BZD/RD have not been studied frequently. The aim of this academic thesis was to describe and analyse relationships between the use of BZD/RD alone or concomitantly with antipsychotics, antidepressants, opioids, antiepileptics, opioids and anticholinergics in the aged and their health. Especially, the relationships between long-term use of BZD/RD and cognitive decline were studied. Additionally, the effect of melatonin on BZD/RD withdrawal and the cognitive effects of BZD/RD withdrawal were studied. This study used multiple data sets: the first study (I) was based on clinical data containing aged patients (≥65 years; N=164) admitted to Pori City Hospital due to acute disease. The second data set (Studies II and III) was based on population-based data from the Lieto Study, a clinico-epidemiological longitudinal study carried out among the aged (≥65 years) in the municipality of Lieto. Follow-up data was formed by combining the cohort data collected in 1990-1991 (N=1283) and in 1998-1999 (N=1596) from those who participated in both cohorts (N=617). The third data set (Studies IV and V) was based on the Satauni Study’s data. This study was performed in the City of Pori in 2009-2010. In the RCT part of the Satauni Study, ninety-two long-term users of BZD/RD were withdrawn from their drugs using melatonin against placebo. The change of their cognitive abilities was measured during and after BZD/ RD withdrawal. BZD/RD use was related to worse cognitive and functional abilities, and their use may predict worse cognitive outcomes compared with BZD/RD non-users. Hypnotic use of BZD/RD could be withdrawn with psychosocial support in motivated participants, but melatonin did not improve the withdrawal results compared to those with placebo. Cognitive abilities in psychomotor tests did not show, or showed only modest, improvements for up to six months after BZD/RD withdrawal. This suggests that the cognitive effects of BZD/RD may be longlasting or permanent.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

Detection of Epilepsy with a Commercial EEG Headband

Epilepsy is a chronic brain disorder, characterized by reoccurring seizures. Automatic sei-zure detector, incorporated into a mobile closed-loop system, can improve the quality of life for the people with epilepsy. Commercial EEG headbands, such as Emotiv Epoc, have a potential to be used as the data acquisition devices for such a system. In order to estimate that potential, epileptic EEG signals from the commercial devices were emulated in this work based on the EEG data from a clinical dataset. The emulated characteristics include the referencing scheme, the set of electrodes used, the sampling rate, the sample resolution and the noise level. Performance of the existing algorithm for detection of epileptic seizures, developed in the context of clinical data, has been evaluated on the emulated commercial data. The results show, that after the transformation of the data towards the characteristics of Emotiv Epoc, the detection capabilities of the algorithm are mostly preserved. The ranges of acceptable changes in the signal parameters are also estimated.