Incremental Information Gain Mining Of Temporal Relational Streams
This paper studies the problem of mining for data values with high information gain in relational tables. High information gain can help data analysts and secondary data mining algorithms gain insights into strong statistical dependencies and causality relationship between key metrics. In this paper, we will study the problem of high information gain identification for scenarios involving temporal relations where new records are added continuously to the relations. We show that information gain can be efficiently maintained in an incremental fashion, making it possible to monitor continuously high information gain values.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Mining Rules Incrementally over Large Knowledge Bases
Multiple web-scale Knowledge Bases, e.g., Freebase, YAGO, NELL, have been constructed using semi-supervised or unsupervised information extraction techniques and many of them, despite their large sizes, are continuously …
Incremental Evaluation and Training in Relational Deep Learning
Relational Deep Learning (RDL) models multi-tabular databases as temporal heterogeneous graphs to enable end-to-end representation learning. However, prevailing RDL evaluation practices rely on static, single-episode dat…
Representation LearningTransfer LearningMasked Relation Learning for DeepFake Detection
Abstract— DeepFake detection aims to differentiate falsified faces from real ones. Most approaches formulate it as a binary classification problem by solely mining the local artifacts and inconsistencies of face forge…
Binary ClassificationDeepFake DetectionFace SwappingGraph Classification+1Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams
Despite growing interest in data stream mining the most successful incremental learners, such as VFDT, still use periodic recomputation to update attribute information gains and Gini indices. This note provides simple in…
AttributeSpatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. …
Anomaly DetectionChange DetectionClusteringEpidemiology+1