Nicolas Méger

Constraint-based mining of episode rules and optimal window sizes

Episode rules are patterns that can be extracted from a large event sequence, to suggest to experts possible dependencies among occurrences of event types. The corresponding mining approaches have been designed to find rules under a temporal constraint that specifies the maximum elapsed time between the first and the last event of the occurrences of the patterns (i.e., a window size constraint). In some applications the appropriate window size is not known, and furthermore, this size is not the same for different rules. To cope with this class of applications, it has been recently proposed in [2] to specifying the maximal elapsed time between two events (i.e., a maximum gap constraint) instead of a window size constraint. Unfortunately, we show that the algorithm proposed to handle the maximum gap constraint is not complete. In this paper we present a sound and complete algorithm to mine episode rules under the maximum gap constraint, and propose to find, for each rule, the window size corresponding to a local maximum of confidence. We show that the extraction can be efficiently performed in practice on real and synthetic datasets. Finally the experiments show that the notion of local maximum of confidence is significant in practice, since no local maximum are found in random datasets, while they can be found in real ones.

Unsupervised Spatiotemporal Mining of Satellite Image Time Series Using Grouped Frequent Sequential Patterns

An important aspect of satellite image time series is the simultaneous access to spatial and temporal information. Various tools allow end users to interpret these data without having to browse the whole data set. In this paper, we intend to extract, in an unsupervised way, temporal evolutions at the pixel level and select those covering at least a minimum surface and having a high connectivity measure. To manage the huge amount of data and the large number of potential temporal evolutions, a new approach based on data-mining techniques is presented. We have developed a frequent sequential pattern extraction method adapted to that spatiotemporal context. A successful application to crop monitoring involving optical data is described. Another application to crustal deformation monitoring using synthetic aperture radar images gives an indication about the generic nature of the proposed approach.

Swap Randomization of Bases of Sequences for Mining Satellite Image Times Series

Swap randomization has been shown to be an effective technique for assessing the significance of data mining results such as Boolean matrices, frequent itemsets, correlations or clusterings. Basically, instead of applying statistical tests on selected attributes, the global structure of the actual dataset is taken into account by checking whether obtained results are likely or not to occur in randomized datasets whose column and row margins are equal to the ones of the actual dataset. In this paper, a swap randomization approach for bases of sequences is proposed with the aim of assessing sequential patterns extracted from Satellite Image Time Series SITS. This assessment relies on the spatiotemporal locations of the extracted patterns. Using an entropy-based measure, the locations obtained on the actual dataset and a single swap randomized dataset are compared. The potential and generality of the proposed approach is evidenced by experiments on both optical and radar SITS.

Mining pixel evolutions in satellite image time series for agricultural monitoring

In this paper, we present a technique to help the experts in agricultural monitoring, by mining Satellite Image Time Series over cultivated areas. We use frequent sequential patterns extended to this spatiotemporal context in order to extract sets of connected pixels sharing a similar temporal evolution. We show that a pixel connectivity constraint can be partially pushed to prune the search space, in conjunction with a support threshold. Together with a simple maximality constraint, the method reveals meaningful patterns in real data.

Extraction of frequent grouped sequential patterns from Satellite Image Time Series

This paper presents an original data mining approach for extracting pixel evolutions and sub-evolutions from Satellite Image Time Series. These patterns, called frequent grouped sequential patterns, represent the (sub-)evolutions of pixels over time, and have to satisfy two constraints: firstly to correspond to at least a given minimum surface and secondly to be shared by pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. Successful experiments on an optical and a radar SITS are presented.

Integrity Constraints over Association Rules

In this paper, we propose to investigate the notion of integrity constraints in inductive databases. We advocate that integrity constraints can be used in this context as an abstract concept to encompass common data mining tasks such as the detection of corrupted data or of patterns that contradict the expert beliefs. To illustrate this possibility we propose a form of constraints called association map constraints to specify authorized confidence variations among the association rules. These constraints are easy to read and thus can be used to write clear specifications. We also present experiments showing that their satisfaction can be tested in practice.

Polsar RADARSAT-2 Satellite Image Time Series mining over the Chamonix Mont-Blanc test site

This paper presents a data mining approach for describing Satellite Image Time Series (SITS) spatially and temporally. It relies on pixel-based evolution and sub-evolution extraction. These evolutions, namely the {frequent grouped sequential patterns}, are required to cover a minimum surface and to affect pixels that are sufficiently connected. These spatial constraints are actively used to face large data volumes and to select evolutions making sense for end-users. In this paper, a specific application to fully polarimetric SAR image time series is presented. Experiments performed on a RADARSAT-2 SITS covering the Chamonix Mont-Blanc test-site are used to illustrate the proposed approach.

SITS-P2miner: Pattern-Based Mining of Satellite Image Time Series

This paper presents a mining system for extracting patterns from Satellite Image Time Series. This system is a fully-fledged tool comprising four main modules for pre-processing, pattern extraction, pattern ranking and pattern visualization. It is based on the extraction of grouped frequent sequential patterns and on swap randomization.

A swap randomization approach for mining motion field time series over the Argentiere glacier

Grouped Frequent Sequential patterns can be extracted in an unsupervised way from Image Time Series (ITS). Plotting the occurrence maps of these patterns allows to describe the dataset spatially and temporally while discarding random uncertainties. However these maps can be too numerous and a swap randomization ranking approach has been proposed recently to select the most promising patterns. This previous work experimented the technique on Satellite ITS, giving credit to the maps that are least likely to appear on a randomized ITS. In this paper, extraction and ranking of GFS patterns is performed on a motion field time series obtained by terrestrial photogrammetry over the Argentière glacier. The focus is extended to the maps that are most likely to occur on the randomized time series and the experiment is repeated thousand times to assess the stability of the ranking.

Iterative summarization of satellite image time series

In this paper we present a method to summarize a satellite image time series. This summary is a small set of maps depicting salient phenomena occurring in the series over space and time. The approach is composed of a first step of extraction of spatiotemporal patterns, followed by an iterative ranking of these patterns using a swap randomization technique and a ranking based on a normalized mutual information measure. The best ranked patterns in the earliest iterations are in some sense the most informative and are used to build the summary. We present results showing that the approach is effective on both optical and radar data.