Mining Predictive Patterns and Extension to Multivariate Temporal Data

An important goal of knowledge discovery is the search for patterns in the data that can help explaining its underlying structure. To be practically useful, the discovered patterns should be novel (unexpected) and easy to understand by humans. In this thesis, we study the problem of mining patterns (defining subpopulations of data instances) that are important for predicting and explaining a specific outcome variable. An example is the task of identifying groups of patients that respond better to a certain treatment than the rest of the patients. We propose and present efficient methods for mining predictive patterns for both atemporal and temporal (time series) data. Our first method relies on frequent pattern mining to explore the search space. It applies a novel evaluation technique for extracting a small set of frequent patterns that are highly predictive and have low redundancy. We show the benefits of this method on several synthetic and public datasets. Our temporal pattern mining method works on complex multivariate temporal data, such as electronic health records, for the event detection task. It first converts time series into time-interval sequences of temporal abstractions and then mines temporal patterns backwards in time, starting from patterns related to the most recent observations. We show the benefits of our temporal pattern mining method on two real-world clinical tasks

FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences

We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on eight real-world datasets demonstrates the effectiveness of our methods in practice. The results provide evidence that FIBS effectively represents IBTSs for classification algorithms, which contributes to similar or significantly better accuracy compared to state-of-the-art competitors. It also suggests that the feature selection strategy is beneficial to FIBS's performance.Comment: In: Big Data Analytics and Knowledge Discovery. DaWaK 2020. Springer, Cha

Discovering Tight Space-Time Sequences

International audienceThe problem of discovering spatiotemporal sequential patterns affects a broad range of applications. Many initiatives find sequences constrained by space and time. This paper addresses an appealing new challenge for this domain: find tight space-time sequences, i.e., find within the same process: i) frequent sequences constrained in space and time that may not be frequent in the entire dataset and ii) the time interval and space range where these sequences are frequent. The discovery of such patterns along with their constraints may lead to extract valuable knowledge that can remain hidden using traditional methods since their support is extremely low over the entire dataset. We introduce a new Spatio-Temporal Sequence Miner (ST SM) algorithm to discover tight space-time sequences. We evaluate ST SM using a proof of concept use case. When compared with general spatial-time sequence mining algorithms (GST SM), ST SM allows for new insights by detecting maximal space-time areas where each pattern is frequent. To the best of our knowledge, this is the first solution to tackle the problem of identifying tight space-time sequences

Constraining the Search Space in Temporal Pattern Mining

Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level

Multivariate time series classification with temporal abstractions

The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved