Dendrogramix: a Hybrid Tree-Matrix Visualization Technique to Support Interactive Exploration of Dendrograms Renaud Blanch, Rémy Dautriche, Gilles Bisson To cite this version: Renaud Blanch, Rémy Dautriche, Gilles Bisson. Dendrogramix: a Hybrid Tree-Matrix Visualization Technique to Support Interactive Exploration of Dendrograms. 8th IEEE Pacific Visualization Sym- posium (PacificVis 2015), 2015, Hangzhou, China. pp.31-38, 10.1109/PACIFICVIS.2015.7156353. hal-01492572 HAL Id: hal-01492572 https://hal.archives-ouvertes.fr/hal-01492572 Submitted on 20 Mar 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. easy for users topairwise figure comparison out of how the items.group classification items is AHC into built, clusters is but asAgglomerative widely also hierarchical soon used clustering as because a (AHC) similarity [ it metric is enables the ology and Techniques —Interaction techniques HCI)]: User Interfaces—GUI; I.3.6 [Computer Graphics]:Index Terms: Method- Dendrogramix, hybrid visualization, interaction. Keywords: tering results. interaction techniques that facilitatesetc. the exploration Those of sensemaking large tasksobjects clus- that are could supported have by been classifiedparticular a cluster; in consistent to a elicit set totally and different of understand cluster); representation, uncommon like: patterns to (e.g., explain whyters a particular and objects individual belongs objects to that a Dendrogramix are impracticable with thetionship classical between individual objects onteractive to visualization the of hierarchy dendrograms ofcontent; that clusters. etc. superimposes the rela- Weat present a given depth); towhich subtrees give those are clusters actual alink clusters representation name (e.g., used by by by inspecting cutting experts their known the for dendrogram as various tasks a like:the to data decide set buterarchical a clustering hierarchy ofdata clusters set. organized in A a wideClustering family binary is often of tree, a cluster first analysis step algorithms, when namely trying to make sense of a large A 1 I BSTRACT ‡ † ∗ e-mail: [email protected] e-mail: [email protected] e-mail: [email protected] Jo Wood Jing Hua NTRODUCTION Min Chen Wei Chen Wei Shixia Liu Ivan Viola Alark Joshi Tim Dwyer Tim Ming Dong Jian Huang Marc Streit Marc Weiwei Cui Weiwei Yingcai Wu Yingcai Ingrid Hotz Hong Zhou Huamin Qu John Stasko Patric Ljung Patric Frits H. Post H. Frits Chi-Wing Fu Chi-Wing Jeffrey Heer Jeffrey Xiaoru Yuan Thomas Ertl Jinwook Seo Song Zhang Jason Dykes Justin Talbot Hans Hagen Xianfeng Gu Hamish Carr Chaoli Wang Melanie Tory Teng-Yok Lee Teng-Yok Kwan-Liu Ma Zhicheng Liu Juliana Freire Miriah Meyer Won-Ki Jeong Won-Ki Jürgen Waser Jürgen Heike Jänicke Heike Philipp Muigg Klaus Mueller Bongshin Lee Paolo Cignoni Paolo Arie Kaufman Danyel Fisher Pat Hanrahan Pat Kenneth I.Joy Kenneth Remco Chang Remco Timo Ropinski Timo Charl P. Botha Charl P. Han-Wei Shen Han-Wei Torsten Moller Torsten Robert Kosara Robert Tino Weinkauf Tino Michael Burch Klaus Hinrichs Alexander Lex Alexander David S.Ebert Ronald Peikert Ronald Anna Vilanova Holger Theisel Eduard Gröller Eduard Helwig Hauser Raphael Fuchs Raphael Bohyoung Kim Petra Isenberg Petra Robert Kincaid Robert Daniel A. Keim Daniel A. Eugene Zhang Aidan Slingsby Jens Schneider Attila Gyulassy Attila Bernd Hamann Bernd Frank van Ham Frank Cláudio T. Silva Cláudio T. Aditi Majumder Aditi Xavier Tricoche Denis Gracanin Vijay Natarajan Vijay Anders Persson Bernhard Preim Bernhard Alireza Entezari Alireza Andrew Hanson Andrew Peter Lindstrom Peter Thomas Schultz Ming-Yuen Chan Ming-Yuen Christoph Garth Valerio Pascucci Valerio Daniel Weiskopf Jarke J.van Wijk Jarke Robert Laramee Robert Stefan Bruckner Michael Bostock Michael Garland Carlos D. Correa Christoph Heinzl Helmut Doleisch Ross T. Whitaker T. Ross William Ribarsky William Michael Gleicher Tamara Munzner Tamara David H. Laidlaw David H. Claes Lundström Marcel Breeuwer Marcel David Thompson Gregory Cipriano Gregory Robert Moorhead Robert Markus Hadwiger Markus Hanspeter Pfister Sebastian Grottel Alexander Wiebel Alexander Robert Mike Kirby Mike Robert Hans-Peter Seidel Hans-Peter Peer-Timo Bremer Peer-Timo Kresimir Matkovic Kresimir Ben Shneiderman Anders Ynnerman Christophe Hurter George Robertson George Ezekiel S. Bhasker Charles D.Hansen Jean-Daniel Fekete Martin Wattenberg Christopher Collins Carl-Fredrik Westin Carl-Fredrik Maneesh Agrawala Michael J. McGuffin Dieter Schmalstieg Fernanda B.Viégas Fernanda Benjamin Schindler Gerik Scheuermann To appear in IEEE Transactions on Visualization and Computer Graphics (Proceedings of PacificVis 2015). Javier OlivánBescós Hans-Christian Hege Luis Gustavo Nonato Luis Rüdiger Westermann Rüdiger Gordon L. Kindlmann Gordon Nathalie Henry Riche Anastasia Bezerianos Sheelagh Carpendale Fernando V. Paulovich V. Fernando Bart ter Haar Romeny Carlos E. Scheidegger dendrogram Dendrogramix: a Hybrid Tree-Matrix Visualization Technique Agglomerative hierarchical clustering, dendrogram, H.5.2 [Information Interfaces and Presentation (e.g., enables users to do tasks which involve both clus- Figure 1: Dendrogramix visualizing 6 years (2006–2011) of co-authorship at the IEEE InfoVis conference. F-38000 Grenoble, France Univ. Grenoble Alpes, LIG to Support Interactive Exploration of Dendrograms algorithms, does not provide a partition of Renaud Blanch . The dendrogram has a classical node- Dendrogramix , a hybrid tree-matrix in- 17 ∗ ] is often used to Univ. Grenoble Alpes, LIG F-38920 Crolles, France Rémy Dautriche STMicroelectronics hi- 1 ferences from the AHC ofthe authors result from of six the past AHC.users (2006–2011) to Figure InfoVis explore con- the hierarchywith a of set clusters of in carefully order designedbut interaction to also techniques make of which sense allows the of a similarity visualization between items. of the ThisDendrogramix visualization hierarchy comes of clusters, of their homogeneity, with any other member ofother the item cluster? of theended in cluster, a or specific is cluster,ble is it to it answer because because some it it kind isthem. is of very questions, similar Without not like: to this very a why informationmation dissimilar single did in on a items, the specific and dendrogram, item thus itwidth)—, does not is the allow not any dendrogram possi- comparison discardstheir involving homogeneity completely (their the height), but original also infor- by their cardinality (their hierarchy in order to producehomogeneity a helps final partition the into userheight actual clusters. of to the choose internal aan nodes). information level This about at the visual which homogeneity encodingclusters of to built of each by the cut cluster the (given cluster’s AHC the by (given by the its node-link structure), but also resulting from an AHC: therelies dendrogram heavily (e.g., on Figure the canonical visualizationtion of has the been cluster made. hierarchy because The they understanding can that choose the users number can of get clusters of after AHC the classifica- to the similarity of theirleast set 3 of papers co-authors (among 512) over this period are grouped according result and the detailsdrogramix about is not items, the but firstThe attempt previous clustering at works showing itself in together uses the this the clustering area well-known AHC method [ the distance between clusters uses the single-linkage method. braries, and manually curated for author deduplication. 2 R In this paper, we introduce an alternative to dendrogram, namely If the dendrogram visualization allows to compare clusters —by The dendrogram provides a visualization of the binary tree of the 2 1 In this example, the measure of similarity is theThe cosine data similarity, set and has been automatically extracted from on-line digital li- ELATED † 1 . The 143 authors (among 1142) that have published at , which provides, within the same screen real estate, W ORK F-38000 Grenoble, France 1 shows the Dendrogramix resulting Gilles Bisson CNRS, LIG 2 ‡ . 2 .b). 17 ]. Den- To appear in IEEE Transactions on Visualization and Computer Graphics (Proceedings of PacificVis 2015). all rely on the juxtaposition of two visualizations: the classical den- E drogram and another visualization for the individual items. Den- C D drogramix is not a juxtaposition of two visualizations but rather a mix of two visualizations. It is thus more close to recent works in the InfoVis community about hybrid visualizations. The interaction B techniques provided by our Dendrogramix are also comparable to A BDECA recent works focused on the interaction with visualizations, espe- cially interaction that exploit the structure of the data visualized to guide the user. (a) (b) 2.1 Hierarchical Clustering Visualization Figure 2: Data set used for illustration purpose in Figure3: (a) points of the plan; and (b) dendrogram of the data using Euclidean distance Several visualization have been proposed to display the information and single-linkage agglomerative hierarchical clustering. about items together