Tiler: Software for human-guided data exploration

Andreas Henelius*, Emilia Oikarinen, Kai Puolamäki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)


Understanding relations in datasets is important for the successful application of data mining and machine learning methods. This paper describes tiler, a software tool for interactive visual explorative data analysis realising the interactive Human-Guided Data Exploration framework. tiler allows a user to formulate different hypotheses concerning the relations in a dataset. Data samples corresponding to these hypotheses are then compared visually, allowing the user to gain insight into relations in the dataset. The exploration process is iterative and the user gradually builds up his or her understanding of the data. Code related to this paper is available at: https://github.com/aheneliu/tiler.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Proceedings
EditorsUlf Brefeld, Alice Marascu, Fabio Pinelli, Edward Curry, Brian MacNamee, Neil Hurley, Elizabeth Daly, Michele Berlingerio
Number of pages5
ISBN (Print)9783030109967
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Conference publication
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Dublin, Ireland
Duration: 10 Sept 201814 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11053 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
Abbreviated titleECML PKDD
Internet address


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