Spatio-temporal c1ustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographie information scienees due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal c1ustering in geographic space. First, we provide a c1assification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal c1ustering -trajectory c1ustering, provide an overview of the state-of-the-art approach es and methods of spatio-temporal c1ustering and finally present several scenarios in different application domains such as movement, ceIIular networks and environmental studies.

This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractMovement data link together space, time, and objects positioned in space and time. They hold valuable and multifaceted information about moving objects, properties of space and time as well as events and processes occurring in space and time. We present a conceptual framework that describes in a systematic and comprehensive way the possible types of information that can be extracted from movement data and on this basis defines the respective types of analytical tasks. Tasks are distinguished according to the type of information they target and according to the level of analysis, which may be elementary (i.e. addressing specific elements of a set) or synoptic (i.e. addressing a set or subsets). We also present a taxonomy of generic analytic techniques, in which the types of tasks are linked to the corresponding classes of techniques that can support fulfilling them. We include techniques from several research fields: visualization and visual analytics, geographic information science, database technology, and data mining.We expect the taxonomy to be valuable for analysts and researchers. Analysts will receive guidance in choosing suitable analytic techniques for their data and tasks.Researchers will learn what approaches exist in different fields and compare or relate them to the approaches they are going to undertake.3

Photo-sharing websites such as Flickr and Panoramio contain millions of geotagged images contributed by people from all over the world. Characteristics of these data pose new challenges in the domain of spatio-temporal analysis. In this paper, we define several different tasks related to analysis of attractive places, points of interest and comparison of behavioral patterns of different user communities on geotagged photo data. We perform analysis and comparison of temporal events, rankings of sightseeing places in a city, and study mobility of people using geotagged photos. We take a systematic approach to accomplish these tasks by applying scalable computational techniques, using statistical and data mining algorithms, combined with interactive geo-visualization. We provide exploratory visual analysis environment, which allows the analyst to detect spatial and temporal patterns and extract additional knowledge from large geotagged photo collections. We demonstrate our approach by applying the methods to several regions in the world.

This article presents a geovisual analytics approach to discovering people's preferences for landmarks and movement patterns from photos posted on the Flickr website. The approach combines an exploratory spatio-temporal analysis of geographic coordinates and dates representing locations and time of taking photos with basic thematic information available through the Google Maps Web mapping service, and interpretation of the analyzed area. The article describes data aggregation and filtering techniques to reduce the size of the dataset and focuses on information addressing research questions. The results of analysis for the Seattle metropolitan area help to distinguish between sites that are occasionally popular among the photographers and can be considered as potential attractions from sites that are regularly visited and already known as city landmarks. The analysis of photographers' movements across the metropolitan area shows that most photographers' itineraries are short and highly localized.

Abstract-Many applications that employ data mining techniques involve mining data that include private and sensitive information about the subjects. One way to enable effective data mining while preserving privacy is to anonymize the dataset that include private information about subjects before being released for data mining. One way to anonymize dataet is to manipulate its content so that the records adhere to k-anonymity. Two common manipulation techniques used to achieve kanonymity of a dataset are generalization and suppression. Generalization refers to replacing a value with a less specific but semantically consistent value, while suppression refers to not releasing a value at all. Generalization is more commonly applied in this domain since suppression may dramatically reduce the quality of the data mining results if not properly used. However, generalization presents a major drawback as it requires a manually generated domain hierarchy taxonomy for every quasiidentifier in the dataset on which k-anonymity has to be performed. In this paper we propose a new method for achieving kanonymity named K-anonymity of Classification Trees Using Suppression (kACTUS). In kACTUS efficient multi-dimensional suppression is performed, i.e., values are suppressed only on certain records depending on other attribute values, without the need for manually-produced domain hierarchy trees. Thus, in kACTUS we identify attributes that have less influence on the classification of the data records and we suppress them if needed in order to comly with k-anonymity. The kACTUS method was evaluated on ten separate datasets to evaluate its accuracy as compared to other k-anonymity generalization and suppressionbased methods. Encouraging results suggest that kACTUS' predictive performance is better than that of existing k-anonymity algorithms. Specifically, on average the accuracies of TDS, TDR and kADET are lower than kACTUS in 3.5%, 3.3% and 1.9% respectively despite their usage of manually defined domain trees. The accuracy gap is increased to 5.3%, 4.3% and 3.1% respectively when no domain trees are used.