Exploring Temporal Data Using Relational Concept Analysis: An Application to Hydroecology (original) (raw)
Related papers
Exploring Heterogeneous Sequential Data on River Networks with Relational Concept Analysis
Lecture Notes in Computer Science, 2018
Nowadays, many heterogeneous relational data are stored in databases to be further explored for discovering meaningful patterns. Such databases exist in various domains and we focus here on river monitoring. In this paper, a limited number of river sites that make up a river network (seen as a directed graph) is given. Periodically, for each river site three types of data are collected. Our aim is to reveal userfriendly results for visualising the intrinsic structure of these data. To that end, we present an approach for exploring heterogeneous sequential data using Relational Concept Analysis. The main objective is to enhance the evaluation step by extracting heterogeneous closed partially-ordered patterns organised into a hierarchy. The experiments and qualitative interpretations show that our method outputs instructive results for the hydro-ecological domain.
Exploring sequential data with relational concept analysis
2017
Many sequential pattern mining methods have been proposed to discover useful patterns that describe the analysed sequential data. Several of these works have focused on efficiently enumerating all closed partially-ordered patterns (cpo-patterns), that makes their evaluation a laboured task for experts since their number can be large. To address this issue, we propose a new approach, that is to directly extract multilevel cpo-patterns implicitly organised into a hierarchy. To this end, we devise an original method within the Relational Concept Analysis (RCA) framework, referred to as RCA-SEQ, that exploits the structure and properties of the lattices from the RCA output. RCA-SEQ spans five steps: the preprocessing of the raw data; the RCA-based exploration of the preprocessed data; the automatic extraction of a hierarchy of multilevel cpo-patterns by navigating the lattices from the RCA output; the selection of relevant multilevel cpo-patterns; the pattern evaluation done by experts.
Extracting Hierarchies of Closed Partially-Ordered Patterns Using Relational Concept Analysis
Lecture Notes in Computer Science, 2016
This paper presents a theoretical framework for exploring temporal data, using Relational Concept Analysis (RCA), in order to extract frequent sequential patterns that can be interpreted by domain experts. Our proposal is to transpose sequences within relational contexts, on which RCA can be applied. To help result analysis, we build closed partially-ordered patterns (cpo-patterns), that are synthetic and easy to read for experts. Each cpo-pattern is associated to a concept extent which is a set of temporal objects. Moreover, RCA allows to build hierarchies of cpo-patterns with two generalisation levels, regarding the structure of cpo-patterns and the items. The benefits of our approach are discussed with respect to pattern structures.
2017
I would also like to thank my mid-thesis committee, Amedeo NAPOLI and Jens GUST-EDT, for their insightful comments and encouragement. In particular I would like to thank Marianne HUCHARD and Xavier DOLQUES for helping me in the preliminary steps of this thesis. In addition, I am very thankful to Corinne GRAC for her patience and her help in validating the experimental results presented in this thesis. I will forever be thankful to Cornelia TUDORIE and Emilia PECHEANU, my former advisers at the "Dunȃrea de Jos" University of Galat , i (Romania), who were the reason why I decided to go to pursue a career in research. I would like to thank the University of Strasbourg for funding my PhD studies. In addition, I would like to thank ICube and ENGEES administrative stuff. Lastly, I would like to thank my family and my close friends for supporting me throughout these past 3 years of my PhD studies.
Mining Temporal Patterns from Relational Data
2005
Agents in dynamic environments have to deal with world representations that change over time. In order to allow agents to act autonomously and to make their decisions on a solid basis an interpretation of the current scene is necessary. If intentions of other agents or events that are likely to happen in the future can be recognized, the agent's performance can be improved as it can adapt the behavior to the situation. In this work we present an approach which applies unsupervised symbolic learning off-line to a qualitative abstraction in order to create frequent temporal patterns in dynamic scenes. Here, an adaption of a sequential pattern mining algorithm which was presented earlier by the authors is proposed in order to reduce the complexity by handling different aspects (class restrictions, variable unifications, and temporal relations) separatedly first, and then combining the results of the single steps. The work is still in progress-this paper introduces the basic ideas and shows an example run of the implemented system.
The representation of sequential patterns and their projections within Formal Concept Analysis
Nowadays data sets are available in very complex and heterogeneous ways. The mining of such data collections is essential to support many real-world applications ranging from healthcare to marketing. In this work, we focus on the analysis of "complex" sequential data by means of interesting sequential patterns. We approach the problem using an elegant mathematical framework: Formal Concept Analysis (FCA) and its extension based on "pattern structures". Pattern structures are used for mining complex data (such as sequences or graphs) and are based on a subsumption operation, which in our case is defined with respect to the partial order on sequences. We show how pattern structures along with projections (i.e., a data reduction of sequential structures), are able to enumerate more meaningful patterns and increase the computing efficiency of the approach. Finally, we show the applicability of the presented method for discovering and analyzing interesting patients' patterns from a French healthcare data set of cancer patients. The quantitative and qualitative results are reported in this use case which is the main motivation for this work.
Data mining with Temporal Abstractions: learning rules from time series
Data Mining and Knowledge …, 2007
A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.
Pattern Structures and Concept Lattices for Data Mining and Knowledge Processing
Lecture Notes in Computer Science, 2015
This article aims at presenting recent advances in Formal Concept Analysis (2010-2015), especially when the question is dealing with complex data (numbers, graphs, sequences, etc.) in domains such as databases (functional dependencies), data-mining (local pattern discovery), information retrieval and information fusion. As these advances are mainly published in artificial intelligence and FCA dedicated venues, a dissemination towards data mining and machine learning is worthwhile.
A knowledge discovery process for spatiotemporal data: Application to river water quality monitoring
Ecological Informatics, 2014
Rapid population growth, and human activities (such as agriculture, industry, transports,...) development have increased vulnerability risk for water resources. Due to the complexity of natural processes and the numerous interactions between hydro-systems and human pressures, water quality is difficult to be quantified. In this context, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre-processed in order to obtain different spatial proximities. Later, we apply a standard algorithm to extract sequential patterns. Finally we propose a combination of two techniques (1) to filter patterns based on interest measure, and; (2) to group and present them graphically, to help the experts. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and river monitoring pressure data.
A survey of temporal knowledge discovery paradigms and methods
IEEE Transactions on Knowledge and Data Engineering, 2002
AbstractÐWith the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.