Transporto informacinės sistemos privalo greitai apdoroti milžiniškus ir vis didėjančius duomenų kiekius. Kadangi skirtingiems tikslams pasiekti ar skirtingoms išvadoms padaryti reikalingų duomenų kiekis kartais gali skirtis kelias dešimtis ar net kelis šimtus kartų jį optimizavus pavyktų sutaupyti daug laiko ir resursų. Siekiant mažinti duomenų kiekį, neprarandant svarbios informacijos yra naudojamas duomenų grupavimas – objektų priskyrimas tam tikrom grupėm pabal bendrus požymius. Šio darbo tikslas – išanalizuoti ir įvertinti grupavimo algoritmų klases, jų tipinius atstovus bei jų pritaikymą transporto sistemų duomenų grupavimui. / The object of investigation of this paper is data clustering and adjustment of data clustering algorithms to traffic flow control systems. The main goal is to analyze which class of clustering algorithms can perform better with specific traffic data, how much of this data is enough to forecast precise results, how much can we minimize and reduce our data and still get correct results.

Customer Relationship Management (CRM) is one of the biggest problems for many companies today. By analyzing history records (profiles) of its customers, organization can effectively adapt its business activity to users needs and create better products and services. Proper analysis of customer profiles can help to predict the behaviour of the customers. After grouping customer profiles by similar attributes, company can easier handle its interactions with similar users. Such group profiling can also help to identify needs of new customers on their first interaction with the company. The biggest problem in implementing such systems is the management of a vast array of customer data. Data mining technologies can help to solve this problem and help the ebusinesses to better understand their e-customers. This work reviews data mining methods, such as Nearest Neighbors, Decision Trees and Association Rules, which can be effectively used for customers grouping and profiling. A new conceptual model of Users Recognition System is suggested. The new model uses profiles created from customer history records for identifying new customers. The suggested model has been tested experimentally and results prove the possibility of practical application of this model.

In the context of adaptive intellectual learning environment (VLE) possibility of using conceptual clustering algorithms is analyzed, trying to accomplish the ability of software agent to "feel" the changing environment and recognise the states it is in. Agent environment is understood as interface between the user of VLE and the students model, which is stored in the VLE and is constantly changing. Agents ability to "feel" is understood as agents ability to classify students, based on their knowledge level, which changes in the learning process. Using conceptual clustering algorithms found in the literature, we are trying to choose one which is most suited for the problem area, modifying it to model real data.

Pristatomas duomenų gavybos ir grupavimo naudojimas paplitusiose sistemose bei sukurtas IT žinių portalo statistikos prototipas duomenų saugojimui, analizei ir peržiūrai atlikti. Siūlomas statistikos modulis duomenų saugykloje periodiškais laiko momentais vykdantis duomenų transformacijas. Portale prieinami statistiniai duomenys gali būti grupuoti. Sugrupuotą informaciją pateikus grafiškai, duomenys gali būti interpretuojami ir stebimi veiklos mastai. Panašių objektų grupėms išskirti pritaikytas vienas iš žinomiausių duomenų grupavimo metodų – lygiagretusis k-vidurkių metodas. / Presented data mining methods and clustering usage in current statistical systems and created statistics module prototype for data storage, analysis and visualization for IT knowledge portal. In suggested statistics prototype database periodical data transformations are performed. Statistical data accessed in portal can be clustered. Clustered information represented graphically may serve for interpreting information when trends may be noticed. One of the best known data clustering methods – parallel k-means method – is adapted for separating similar data clusters.