Irene Ntoutsi

MONIC: modeling and monitoring cluster transitions

There is much recent work on detecting and tracking change in clusters, often based on the study of the spatiotemporal properties of a cluster. For the many applications where cluster change is relevant, among them customer relationship management, fraud detection and marketing, it is also necessary to provide insights about the nature of cluster change: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Is a new emerging cluster reflecting a new target group of customers or does it rather consist of existing customers whose preferences shift? To answer such questions, we propose the framework MONIC for modeling and tracking of cluster transitions. Our cluster transition model encompasses changes that involve more than one cluster, thus allowing for insights on cluster change in the whole clustering. Our transition tracking mechanism is not based on the topological properties of clusters, which are only available for some types of clustering, but on the contents of the underlying data stream. We present our first results on monitoring cluster transitions over the ACM digital library.

Similarity Search in Trajectory Databases

Trajectory database (TD) management is a relatively new topic of database research, which has emerged due to the explosion of mobile devices and positioning technologies. Trajectory similarity search forms an important class of queries in TD with applications in trajectory data analysis and spatiotemporal knowledge discovery. In contrast to related works which make use of generic similarity metrics that virtually ignore the temporal dimension, in this paper we introduce a framework consisting of a set of distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). The novelty of the approach is not only to provide qualitatively different means to query for similar trajectories, but also to support trajectory clustering and classification mining tasks, which definitely imply a way to quantify the distance between two trajectories. For each of the proposed distance operators we devise highly parametric algorithms, the efficiency of which is evaluated through an extensive experimental study using synthetic and real trajectory datasets.

Building real-world trajectory warehouses

The flow of data generated from low-cost modern sensing technologies and wireless telecommunication devices enables novel research fields related to the management of this new kind of data and the implementation of appropriate analytics for knowledge extraction. In this work, we investigate how the traditional data cube model is adapted to trajectory warehouses in order to transform raw location data into valuable information. In particular, we focus our research on three issues that are critical to trajectory data warehousing: (a) the trajectory reconstruction procedure that takes place when loading a moving object database with sampled location data originated e.g. from GPS recordings, (b) the ETL procedure that feeds a trajectory data warehouse, and (c) the aggregation of cube measures for OLAP purposes. We provide design solutions for all these issues and we test their applicability and efficiency in real world settings.

A unified and flexible framework for comparing simple and complex patterns

One of the most important operations involving Data Mining patterns is computing their similarity. In this paper we present a general framework for comparing both simple and complex patterns, i.e., patterns built up from other patterns. Major features of our framework include the notion of structure and measure similarity, the possibility of managing multiple coupling types and aggregation logics, and the recursive definition of similarity for complex patterns.

Mining Trajectory Databases via a Suite of Distance Operators

With the rapid progress of mobile devices and positioning technologies, Trajectory databases (TD) have been in the core of database research during the last decade. Analysis and knowledge discovery in TD is an emerging field which has recently gained great interest. Extracting knowledge from TD using certain types of mining techniques, such as clustering and classification, impose that there is a mean to quantify the distance between two trajectories. Having as a main objective the support of effective similarity query processing, existing approaches utilize generic distance metrics that ignore the peculiarities of the trajectories as complex spatio-temporal data types. In this paper, we define a novel set of trajectory distance operators based on primitive (space and time) as well as derived parameters of trajectories (speed and direction). Aiming at providing a powerful toolkit for analysts who require producing distance matrices with different semantics as input to mining tasks, we develop algorithms for each of the proposed operators. The efficiency of our approach is evaluated through an experimental study on classification and clustering tasks using synthetic and real trajectory datasets.

gRecs: a group recommendation system based on user clustering

In this demonstration paper, we present gRecs, a system for group recommendations that follows a collaborative strategy. We enhance recommendations with the notion of support to model the confidence of the recommendations. Moreover, we propose partitioning users into clusters of similar ones. This way, recommendations for users are produced with respect to the preferences of their cluster members without extensively searching for similar users in the whole user base. Finally, we leverage the power of a top-k algorithm for locating the top-k group recommendations.

Discovering global and local bursts in a stream of news

Reports on major events like hurricanes and earthquakes, and major topics like the financial crisis or the Egyptian revolution appear in Internet news and become (ir)regularly updated, as new insights are acquired. Tracking emerging subtopics in a major or even local event is important for the news readers but challenging for the operator: subtopics may emerge gradually or in a bursty way; they may be of some importance inside the event, but too rare to be visible inside the whole stream of news. In this study, we propose a text stream clustering method that detects, tracks and updates large and small bursts of news in a two-level topic hierarchy. We report on our first results on a stream of news from February to April 2011.

Density based subspace clustering over dynamic data

Modern data are often high dimensional and dynamic. Subspace clustering aims at finding the clusters and the dimensions of the high dimensional feature space where these clusters exist. So far, the subspace clustering methods are mainly static and cannot address the dynamic nature of modern data. In this paper, we propose a dynamic subspace clustering method, which extends the density based projected clustering algorithm PreDeCon for dynamic data. The proposed method efficiently examines only those clusters that might be affected due to the population update. Both single and batch updates are considered.

Density-based community detection in social networks

This paper deals with community detection in social networks using density-based clustering. We compare two well-known concepts for community detection that are implemented as distance functions in the algorithms SCAN [1] and DEN-GRAPH [2], the structural similarity of nodes and the number of interactions between nodes, respectively, in order to evaluate advantages and limitations of these approaches. Additionally, we propose to use a hierarchical approach for clustering in order to get rid of the problem of choosing an appropriate density threshold for community detection, a severe limitation of the applicability and usefulness of the SCAN and DENGRAPH algorithms in real life applications. We conduct all experiments on data sets with different characteristics, particularly Twitter data and Enron data.

Traffic mining in a road-network: How does the traffic flow?

The flow of data coming from modern sensing devices enables the development of novel research techniques related to data management and knowledge extraction. In this work, we undertake the problem of analysing traffic in a road network so as to help the city authorities to optimise traffic flow. A graph based modelling of the network traffic is presented which provides insights on the flow of movements within the network. We exploit this graph in order to analyse the traffic flow in the network and to discover traffic relationships like propagation, split and merge of traffic among the road segments. First experimental results illustrate the applicability and usefulness of our approach.