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A Visual Approach for Interactive Keyterm-Based Clustering

Authors:

Seyednaser Nourashrafeddin,

Rosane Minghim,

Evangelos E. MiliosAuthors Info & Claims

ACM Transactions on Interactive Intelligent Systems (TiiS), Volume 8, Issue 1

Article No.: 6, Pages 1 - 35

https://doi.org/10.1145/3181669

Published: 20 February 2018 Publication History

Abstract

The keyterm-based approach is arguably intuitive for users to direct text-clustering processes and adapt results to various applications in text analysis. Its way of markedly influencing the results, for instance, by expressing important terms in relevance order, requires little knowledge of the algorithm and has predictable effect, speeding up the task. This article first presents a text-clustering algorithm that can easily be extended into an interactive algorithm. We evaluate its performance against state-of-the-art clustering algorithms in unsupervised mode. Next, we propose three interactive versions of the algorithm based on keyterm labeling, document labeling, and hybrid labeling. We then demonstrate that keyterm labeling is more effective than document labeling in text clustering. Finally, we propose a visual approach to support the keyterm-based version of the algorithm. Visualizations are provided for the whole collection as well as for detailed views of document and cluster relationships. We show the effectiveness and flexibility of our framework, Vis-Kt, by presenting typical clustering cases on real text document collections. A user study is also reported that reveals overwhelmingly positive acceptance toward keyterm-based clustering.

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References

[1]

C. C. Aggarwal and Ch. Zhai. 2012. A survey of text-clustering algorithms. In Mining Text Data. Springer, 77--128.

Digital Library

[2]

D. Andrzejewski, X. Zhu, and M. Craven. 2009. Incorporating domain knowledge into topic modeling via dirichlet forest priors. In Proceedings of the 26th Annual International Conference on Machine Learning (ICML’09). ACM, New York, NY, 25--32.

Digital Library

[3]

P. Awasthi, M. F. Balcan, and K. Voevodski. 2017. Local algorithms for interactive clustering. J. Mach. Learn. Res. 18, 3 (2017), 1--35.

Digital Library

[4]

S. Basu, A. Banerjee, and R. J. Mooney. 2002. Semi-supervised clustering by seeding. In Proceedings of the 19th International Conference on Machine Learning (ICML’02). Morgan Kaufmann, San Francisco, CA, 27--34.

Digital Library

[5]

S. Basu, A. Banerjee, and R. J. Mooney. 2004. Active semi-supervision for pairwise constrained clustering. In Proceedings of the SIAM International Conference on Data Mining. 333--344.

[6]

M. W. Berry, Mu. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons. 2007. Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 52, 1 (2007), 155--173.

[7]

J. Bezdek. 1981. Pattern Recognition with Fuzzy Objective Functions. Kluwer Academic Publishers, Norwell, MA.

Digital Library

[8]

D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993--1022.

[9]

M. Bostock, V. Ogievetsky, and J. Heer. 2011. D3 data-driven documents. IEEE Trans. Visual. Comput. Graph. 17, 12 (2011), 2301--2309.

Digital Library

[10]

L. Boudjeloud-Assala, Ph. Pinheiro, A. Blansché, Th. Tamisier, and B. Otjacques. 2015. Interactive and iterative visual clustering. Information Visualization 15, 3 (2015), 181--197.

Digital Library

[11]

E. T. Brown, J. Liu, C. E. Brodley, and R. Chang. 2012. Dis-function: Learning distance functions interactively. In Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST’12). 83--92.

Digital Library

[12]

J. Choo, C. Lee, C. K. Reddy, and H. Park. 2013. UTOPIAN: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans. Visual. Comput. Graph. 19, 12 (Dec 2013), 1992--2001.

Digital Library

[13]

J. Chuang, C. D. Manning, and J. Heer. 2012. Termite: Visualization techniques for assessing textual topic models. In Proceedings of the International Working Conference on Advanced Visual Interfaces. ACM, New York, 74--77.

Digital Library

[14]

G. V. Cormack, J. M. G. Hidalgo, and E. P. Sánz. 2007. Feature engineering for mobile SMS spam filtering. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’07). ACM, New York, NY, 871--872.

Digital Library

[15]

D. Costa and G. Venturini. 2007. A visual and interactive data exploration method for large data sets and clustering. In Proceedings of the 3rd International Conference on Advanced Data Mining and Applications. Springer-Verlag, 553--561.

Digital Library

[16]

W. Cui, Sh. Liu, L. Tan, C. Shi, Y. Song, Z. J. Gao, H. Qu, and X. Tong. 2011. Textflow: Towards better understanding of evolving topics in text. IEEE Trans. Visual. Comput. Graph. 17, 12 (2011), 2412--2421.

Digital Library

[17]

M. des Jardins, J. MacGlashan, and J. Ferraioli. 2007. Interactive visual clustering. In Proceedings of the 12th International Conference on Intelligent User Interfaces. ACM, New York, 361--364.

Digital Library

[18]

A. Endert, P. Fiaux, and C. North. 2012. Semantic interaction for visual text analytics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI’12). ACM, New York, 473--482.

Digital Library

[19]

A. Endert, S. Fox, D. Maiti, S. Leman, and C. North. 2012. The semantics of clustering: Analysis of user-generated spatializations of text documents. In Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI’12). ACM, New York, 555--562.

Digital Library

[20]

P. Fiaux, M. Sun, L. Bradel, C. North, N. Ramakrishnan, and A. Endert. 2013. Bixplorer: Visual analytics with biclusters. Computer 46, 8 (2013), 90--94.

Digital Library

[21]

Th. M. J. Fruchterman and E. M. Reingold. 1991. Graph drawing by force-directed placement. Softw. Pract. Exper. 21, 11 (Nov. 1991), 1129--1164.

Digital Library

[22]

L. Galavotti, F. Sebastiani, and M. Simi. 2000. Experiments on the use of feature selection and negative evidence in automated text categorization. In Proceedings of the 4th European Conference on Research and Advanced Technology for Digital Libraries. Springer-Verlag, London, UK, 59--68.

Digital Library

[23]

C. Gorg, L. Zhicheng, K. Jaeyeon, Ch. Jaegul, P. Haesun, and J. Stasko. 2013. Combining computational analyses and interactive visualization for document exploration and sensemaking in jigsaw. IEEE Trans. Visual. Comput. Graph. 19, 10 (Oct 2013), 1646--1663.

[24]

D. Greene and P. Cunningham. 2006. Practical solutions to the problem of diagonal dominance in kernel document clustering. In Proceedings of the 23rd International Conference on Machine Learning. ACM Press, 377--384.

Digital Library

[25]

F. Heimerl, S. Lohmann, S. Lange, and T. Ertl. 2014. Word cloud explorer: Text analytics based on word clouds. In Proceedings of the 47th Hawaii International Conference on System Sciences (HICSS’14). 1833--1842.

Digital Library

[26]

E. Hoque and G. Carenini. 2016. Interactive topic modeling for exploring asynchronous online conversations: Design and evaluation of ConVisIT. ACM Trans. Interact. Intell. Syst. 6, 1, Article 7 (Feb. 2016), 24 pages.

Digital Library

[27]

X. Hu, L. Bradel, D. Maiti, L. House, C. North, and S. Leman. 2013. Semantics of directly manipulating spatializations. IEEE Trans. Visual. Comput. Graph. 19, 12 (Dec 2013), 2052--2059.

Digital Library

[28]

Y. Hu, B. Boyd-Graber, and J. Satinoff, and A. Smith. 2014. Interactive topic modeling. Mach. Learn. 95, 3 (2014), 423--469.

Digital Library

[29]

Y. Hu, E. Milios, and J. Blustein. 2011. Interactive feature selection for document clustering. In Proceedings of the 2011 ACM Symposium on Applied Computing (SAC’11). ACM, New York, 1143--1150.

Digital Library

[30]

Y. Hu, E. Milios, and J. Blustein. 2012. Enhancing semi-supervised document clustering with feature supervision. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC’12). ACM, New York, 929--936.

Digital Library

[31]

Y. Hu, E. Milios, and J. Blustein. 2012. Semi-supervised document clustering with dual supervision through seeding. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC’12). ACM, New York, NY, 144--151.

Digital Library

[32]

Y. Hu, E. Milios, J. Blustein, and S. Liu. 2012. Personalized document clustering with dual supervision. In Proceedings of the 2012 ACM Symposium on Document Engineering (DocEng’12). ACM, New York, 161--170.

Digital Library

[33]

J. Kogan, C. Nicholas, and V. Volkovich. 2003. Text mining with information-theoretic clustering. Comput. Sci. Eng. 5, 6 (Nov. 2003), 52--59.

Digital Library

[34]

B. Larsen and Ch. Aone. 1999. Fast and effective text mining using linear-time document clustering. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’99). ACM, New York, NY, 16--22.

Digital Library

[35]

H. Lee, J. Kihm, J. Choo, J. Stasko, and H. Park. 2012. iVisClustering: An interactive visual document clustering via topic modeling. Comput. Graph. Forum 31, 3pt3 (2012), 1155--1164.

Digital Library

[36]

L. V. D. Maaten and G. E. Hinton. 2008. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9 (2008), 2579--2605.

[37]

C. D. Manning, P. Raghavan, and H. Schütze. 2008. Flat clustering. In Introduction to Information Retrieval. Cambridge University Press, New York, NY, 253--287.

Digital Library

[38]

Y. Matsuo and M. Ishizuka. 2004. Keyword extraction from a single document using word co-occurrence statistical information. Int. J. Artific. Intell. Tools 13, 01 (2004), 157--169.

[39]

J. L. Neto, A. D. Santos, C. A. A. Kaestner, and A. A. Freitas. 2000. Document clustering and text summarization. In Proceedings of the 4th International Conference Practical Applications of Knowledge Discovery and Data Mining (PADD’00).

[40]

S. Nourashrafeddin. 2014. Interactive Term Supervised Text Document Clustering. Ph.D. Dissertation. Faculty of Computer Science, Dalhousie University, Retrieved from https://dalspace.library.dal.ca/handle/10222/55965.

[41]

S. Nourashrafeddin, E. Milios, and D. V. Arnold. 2013. An evolutionary algorithm for feature selective double clustering of text documents. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC’13). IEEE, 446--453.

[42]

S. Nourashrafeddin, E. Milios, and D. V. Arnold. 2014. An ensemble approach for text document clustering using wikipedia concepts. In Proceedings of the 2014 ACM Symposium on Document Engineering (DocEng’14). ACM, New York, 107--116.

Digital Library

[43]

S. N. Nourashrafeddin, E. Milios, and D. V. Arnold. 2013. Interactive text document clustering using feature labeling. In Proceedings of the 2013 ACM Symposium on Document Engineering (DocEng’13). ACM, New York, 61--70.

Digital Library

[44]

F. V. Paulovich, L. G. Nonato, R. Minghim, and H. Levkowitz. 2008. Least square projection: A fast high precision multidimensional projection technique and its application to document mapping. IEEE Trans. Visual. Comput. Graph. 14, 3 (2008), 564--575.

Digital Library

[45]

M. F. Porter. 1980. An algorithm for suffix stripping. Progr.: Electron. Libr. Info. Syst. 14, 3 (1980), 130--137.

[46]

A. A. Puretskiy, G. L. Shutt, and M. W. Berry. 2010. Survey of text visualization techniques. Text Mining: Applications and Theory (2010), 105--127.

[47]

D. Ramage, D. Hall, R. Nallapati, and Ch. D. Manning. 2009. Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 (EMNLP’09). Association for Computational Linguistics, Stroudsburg, PA, 248--256.

Digital Library

[48]

R. G. Rossi, R. M. Maracini, and S. O. Rezende. 2014. Analysis of domain independent statistical keyword extraction methods for incremental clustering. Learn. Nonlin. Models 12, 1 (2014), 17--37.

[49]

P. J. Rousseeuw. 1987. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20 (1987), 53--65.

Digital Library

[50]

W. Tang, H. Xiong, Sh. Zhong, and J. Wu. 2007. Enhancing semi-supervised clustering: A feature projection perspective. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07). ACM, New York, 707--716.

Digital Library

[51]

O. Turetken and R. Sharda. 2005. Clustering-based visual interfaces for presentation of web search results: An empirical investigation. Info. Syst. Front. 7, 3 (2005), 273--297.

Digital Library

[52]

H. Xu, Zh. Li, Sh. Guo, and K. Chen. 2012. CloudVista: Interactive and economical visual cluster analysis for big data in the cloud. Proc. VLDB Endow. 5, 12 (2012), 1886--1889.

Digital Library

[53]

Q. You, Sh. Fang, and P. Ebright. 2010. Iterative visual clustering for unstructured text mining. In Proceedings of the International Symposium on Biocomputing. ACM, New York, 26.

Digital Library

Cited By

Cabral ERezaeipourfarsangi SOliveira MMilios EMinghim R(2023)Addressing the gap between current language models and key-term-based clusteringProceedings of the ACM Symposium on Document Engineering 202310.1145/3573128.3604900(1-10)Online publication date: 22-Aug-2023
https://dl.acm.org/doi/10.1145/3573128.3604900
Wang ZWortman Vaughan JCaruana RChau D(2023)GAM Coach: Towards Interactive and User-centered Algorithmic RecourseProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580816(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580816
da Silva SMilios Ede Oliveira M(2023)Evaluating Visual Analytics for Relevant Information Retrieval in Document CollectionsInteracting with Computers10.1093/iwc/iwad01935:2(247-261)Online publication date: 3-Mar-2023
https://doi.org/10.1093/iwc/iwad019
Show More Cited By

Index Terms

A Visual Approach for Interactive Keyterm-Based Clustering
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics
2. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering

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Information & Contributors

Information

Published In

cover image ACM Transactions on Interactive Intelligent Systems

ACM Transactions on Interactive Intelligent Systems Volume 8, Issue 1

Special Issue on Interactive Visual Analysis of Human Crowd Behaviors and Regular Paper

March 2018

132 pages

ISSN:2160-6455

EISSN:2160-6463

DOI:10.1145/3185338

Editor:
Michelle Zhou
Juji, Inc., USA

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Copyright © 2018 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 February 2018

Accepted: 01 December 2017

Revised: 01 June 2017

Received: 01 October 2016

Published in TIIS Volume 8, Issue 1

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Boeing Company, CNPq and FAPESP (Brazil)
Natural Sciences and Engineering Research Council of Canada
International Development Research Centre, Ottawa, Canada

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Cited By

Cabral ERezaeipourfarsangi SOliveira MMilios EMinghim R(2023)Addressing the gap between current language models and key-term-based clusteringProceedings of the ACM Symposium on Document Engineering 202310.1145/3573128.3604900(1-10)Online publication date: 22-Aug-2023
https://dl.acm.org/doi/10.1145/3573128.3604900
Wang ZWortman Vaughan JCaruana RChau D(2023)GAM Coach: Towards Interactive and User-centered Algorithmic RecourseProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580816(1-20)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580816
da Silva SMilios Ede Oliveira M(2023)Evaluating Visual Analytics for Relevant Information Retrieval in Document CollectionsInteracting with Computers10.1093/iwc/iwad01935:2(247-261)Online publication date: 3-Mar-2023
https://doi.org/10.1093/iwc/iwad019
Handler AMahyar NO’Connor B(2022)ClioQuery: Interactive Query-oriented Text Analytics for Comprehensive Investigation of Historical News ArchivesACM Transactions on Interactive Intelligent Systems10.1145/352402512:3(1-49)Online publication date: 26-Jul-2022
https://dl.acm.org/doi/10.1145/3524025
Takama YTanaka YMori YShibata H(2021)Treemap-Based Cluster Visualization and its Application to Text Data AnalysisJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2021.p049825:4(498-507)Online publication date: 20-Jul-2021
https://doi.org/10.20965/jaciii.2021.p0498
da Silva SMilios Ede Oliveira MMonteiro Ida Hora Rodrigues Kde Gois Ribeiro Darin TFreire AMota M(2021)Evaluating visual analytics for text information retrievalProceedings of the XX Brazilian Symposium on Human Factors in Computing Systems10.1145/3472301.3484320(1-11)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3472301.3484320
Neubauer TPeres SFantinato MLu XReijers H(2021)Interactive clustering: a scoping reviewArtificial Intelligence Review10.1007/s10462-020-09913-754:4(2765-2826)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s10462-020-09913-7
Shneiderman B(2020)Bridging the Gap Between Ethics and PracticeACM Transactions on Interactive Intelligent Systems10.1145/341976410:4(1-31)Online publication date: 16-Oct-2020
https://dl.acm.org/doi/10.1145/3419764
Cabral EMilios EMinghim R(2020)Visual analysis of interactive document clustering streamsProceedings of the 2020 International Conference on Advanced Visual Interfaces10.1145/3399715.3399962(1-3)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3399715.3399962
Nguyen NCoustaty MOgier J(2020)An adaptive document recognition system for lettrinesInternational Journal on Document Analysis and Recognition10.1007/s10032-019-00346-923:2(115-128)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10032-019-00346-9
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