Abstract
Clustering is able to present a brief illustration for families of interest and patterns of significance within large-scale genealogical datasets. In the traditional clustering methods, topological features are mostly taken for summarizing and organizing family trees. However, plentiful attributes are ignored which are also important to enhance the understanding and interpretation of genealogical clustering features. Thus, it is a crucial task to combine structures and attributes into a clustering model for exploring genealogy datasets. In this paper, we propose an attribute-enhanced topological clustering method for exploring genealogy datasets based on partial least squares (PLS). Firstly, a graphlet kernel method is utilized to measure the structure difference between family trees. Then, we leverage PLS to combine the learned vectors and multiple attributes, and a joint dimensionality reduction method is applied to project the high-dimensional vectors into a two-dimensional space in which a distance-based clustering method is employed to aggregate the similar family trees taking both the topological structures and attribute features into consideration. Further, we implement a visual analysis system with multi-view collaboration, including glyph, family tree view and parallel coordinate view, to represent, evaluate and explore the clustering features. Case studies and quantitative comparisons based on real-world genealogy datasets have demonstrated the effectiveness of our method in genealogical clustering and exploration.
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References
Bezerianos A, Dragicevic P, Fekete JD, Bae J, Watson B (2011) GeneaQuilts: a system for exploring large genealogies. IEEE Trans vis Comput Graph 16:1073–1081. https://doi.org/10.1109/TVCG.2010.159
Boudjeloud-Assala L, Pinheiro P, Blansch A, Tamisier T, Otjacques B (2016) Interactive and iterative visual clustering. Inf vis 15(3):181–197
Cao N, Gotz D, Sun J, Qu H (2011) DICON: interactive visual analysis of multidimensional clusters. In: IEEE transactions on visualization and computer graphics, vol 17, no 12. pp 2581–2590. https://doi.org/10.1109/TVCG.2011.188
Chen K, Liu L (2004) VISTA: validating and refining clusters via visualization. Inf vis 3(4):257–270
Davies DL, Bouldin DW (1979) A cluster separation measure. In: IEEE transactions on pattern analysis and machine intelligence, vol PAMI-1, no 2, pp 224–227. https://doi.org/10.1109/TPAMI.1979.4766909
Dexter S, Yarmish G, Listowsky P (2016) Parallel clustering of protein structures generated via stochastic Monte Carlo. In: proceedings of symposium on stochastic models in reliability engineering. Life Science and Operations Management (SMRLO), pp 410–413. https://doi.org/10.1109/SMRLO.2016.71
Fu S, Dong H, Cui W, Zhao J, Qu H (2017) How do ancestral traits shape family trees over generations? In: IEEE transactions on visualization and computer graphics, vol. 24, no 1, pp 205–214. https://doi.org/10.1109/TVCG.2017.2744080
Furnas GW, Zacks J (1994) Multitrees: enriching and reusing hierarchical structure. In: CHI’94: proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 330–336
Grygorash O, Zhou Y, Jorgensen Z (2006) Minimum spanning tree based clustering algorithms. In: Proceedings—international conference on tools with artificial intelligence, ICTAI, pp 73–81. https://doi.org/10.1109/ICTAI.2006.83
Gu T, Zhu M, Chen W et al (2018) Structuring mobility transition with an adaptive graph representation. IEEE Trans Comput Soc Syst 5(4):1121–1132
Hillis DM, Heath TA, John KS (2005) Analysis and visualization of tree space. Syst Biol 54:471–482. https://doi.org/10.1080/10635150590946961
Jin C, Bai Q (2016) Text clustering algorithm based on the graph structures of semantic word co-occurrence. In: 2016 international conference on information system and artificial intelligence (ISAI), pp 497-502. https://doi.org/10.1109/ISAI.2016.0112
Kemp T (1999) Genealogy: finding roots on the web. Coll Res Libr News 60:452–455. https://doi.org/10.5860/crln.60.6.452
Ko S, Afzal S, Walton S, et al (2014) Analyzing high-dimensional multivaríate network links with integrated anomaly detection, highlighting and exploration. In: IEEE conference on visual analytics science and technology (VAST)
Kong X, Chen Y, Tian H, Wang T, Cai Y, Chen X (2016) A novel botnet detection method based on preprocessing data packet by graph structure clustering. In: 2016 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC), pp 42–45. https://doi.org/10.1109/CyberC.2016.16
Kosaka T, Sagayama S (1994) Tree-structured speaker clustering for fast speaker adaptation. In: IEEE international conference on acoustics, pp 245–248. https://doi.org/10.1109/ICASSP.1994.389309
Kozak M (2012) A dendrite method for cluster analysis. Commun Stat Theory Methods 3(1):1–27. https://doi.org/10.1080/03610927408827101
Kutz DO (2004) Examining the evolution and distribution of patent classifications. In: Proceedings of information visualisation, pp 983–988. https://doi.org/10.1109/IV.2004.1320261
Kwon BC, Eysenbach B, Verma J, Ng K, De Filippi C, Stewart WF, Perer A (2017) Clustervision: visual supervision of unsupervised clustering. In: IEEE transactions on visualization and computer graphics. vol 24, no 1, pp 142–151. https://doi.org/10.1109/TVCG.2017.2745085
L’Yi S, Ko B, Shin D, Cho Y-J, Lee J, Kim B, Seo J (2015) XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data. BMC Bioinf 16(11):S5
Laurens VDM, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(2605):2579–2605
Liao H, Wu Y, Chen L, Chen W (2018) Cluster-based visual abstraction for multivariate scatterplots. IEEE Trans vis Comput Graph 24(9):2531–2545. https://doi.org/10.1109/TVCG.2017.2754480
Liu M, Shi J, Li Z, Li C, Zhu J, Liu S (2017a) Towards better analysis of deep convolutional neural networks. IEEE Trans vis Comput Graph 23(1):91–100. https://doi.org/10.1109/TVCG.2016.2598831
Liu S, Cui W, Wu Y, Liu M (2014) A survey on information visualization: recent advances and challenges. Vis Comput 30(12):1373–1393. https://doi.org/10.1007/s00371-013-0892-3
Liu Y, Dai S, Wang C, Zhou Z, Qu H (2017). GenealogyVis: A system for visual analysis of multidimensional genealogical data. In: IEEE
Maguire E, Koutsakis I, Louppe G (2016) Clusterix: a visual analytics approach to clustering. In: Symposium on visualization in data science at IEEE VIS
Munzner T, Guimbretiere F, Tasiran S, Zhang L, Zhou Y (2003) TreeJuxtaposer: scalable tree comparison using focus+context with guaranteed visibility. ACM Trans Graph 22:453–462. https://doi.org/10.1145/1201775.882291
Nober C, Gehlenborg N, Coo H et al (2019) Lineage: visualizing multivariate clinical data in genealogy graphs. Trans vis Comput Graph 25(3):1543–1558
Papadopoulos AN, Manolopoulos Y (1999) Structure-based similarity search with graph histograms. In: Proceedings. 10th international workshop on database and expert systems applications. DEXA 99, pp 174–178. https://doi.org/10.1109/DEXA.1999.795162
Partl C, Gratzl S, Streit M et al (2016) Pathfinder: visual analysis of paths in graphs. Comput Graph Forum J Eur Assoc Comput Graph 35(3):71–80
Rahman M, Bhuiyan MA, Rahman M, Al HM (2014) GUISE: a uniform sampler for constructing frequency histogram of graphlets. Knowl Inf Syst 38:511–536. https://doi.org/10.1007/s10115-013-0673-3
Shaw PD, Graham M, Kennedy J, Milne I, Marshall DF (2014) Helium: visualization of large scale plant pedigrees. BMC Bioinf 15(1):259. https://doi.org/10.1186/1471-2105-15-259
Tsuya NO, Wang F, Alter G, Lee JZ (2010) Prudence and pressure: reproduction and human agency in Europe and Asia, 1700–1900. https://doi.org/10.7551/mitpress/8162.001.0001
Wang Y, Shi C, Li L et al (2018) Visualizing research impact through citation data. ACM Trans Interactive Intell Syst 8(1):1–24
Wattenberg M (2006) Visual exploration of multivariate graphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 811–819. https://doi.org/10.1145/1124772.1124891
White D (1993) Representing and computing kinship—a new approach (VOL 33, PG 454, 1992). Curr Anthropol 34:176–176
Wu W, Xu J, Zeng H, Zheng Y, Qu H, Ni B, Yuan M, Ni LM (2015) TelCoVis: visual exploration of co-occurrence in urban human mobility based on telco data. IEEE Trans vis Comput Graph 22:1–1. https://doi.org/10.1109/TVCG.2015.2467194
Xia JZ, Zhang YH, Ye H, Wang Y, Jiang G, Zhao Y, Xie C, Kui XY, Liao SH, Wang WP (2020a) SuPoolVisor: a visual analytics system for mining pool surveillance. Front Inf Technol Electron Eng 21(4):507–523
Xia et al (2020b) SMAP: a joint dimensionality reduction scheme for secure multi-party visualization. IEEE Conf vis Anal Sci Technol 2020:107–118. https://doi.org/10.1109/VAST50239.2020.00015
Xia J, Ye F, Chen W, Wang Y, Chen W, Ma Y, Tung AK (2018) LDSScanner: exploratory analysis of low-dimensional structures in high-dimensional datasets. IEEE Trans vis Comput Graph 24(1):236–245
Yang M, Wu C, Xie T (2020) Information propagation dynamics model based on implicit cluster structure network. In: Proceedings of IEEE information technology and mechatronics engineering conference, pp 1253–1257. https://doi.org/10.1109/ITOEC49072.2020.9141733
Yuan J, Chen C, Yang W et al (2020) A survey of visual analytics techniques for machine learning. Comput Vis Media 7:3–36. https://doi.org/10.1007/s41095-020-0191-7
Zhang K, Wang JT, Shasha D (2011) On the editing distance between undirected acyclic graphs. Int J Found Comput Sci. https://doi.org/10.1142/S0129054196000051
Zhao Y, Luo X, Lin X, Wang H, Kui X, Zhou F, Wang J, Chen Y, Chen W (2020) Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Trans vis Comput Graph 26(1):590–600. https://doi.org/10.1109/TVCG.2019.2934655
Zhao Y, Jiang H, Qin Y, Xie H, Wu Y, Liu S, Zhou Z, Xia J, Zhou F (2021) Preserving minority structures in graph sampling. IEEE Trans vis Comput Graph 27(2):1–10. https://doi.org/10.1109/TVCG.2020.3030428
Zhou Z, Ye Z, Liu Y et al (2017a) Visual analytics for spatial clusters of air-quality data. IEEE Comput Graph Appl 37(5):98
Zhou Z et al (2021) Context-aware sampling of large networks via graph representation learning. IEEE Trans vis Comput Graph 27(2):1709–1719. https://doi.org/10.1109/TVCG.2020.3030440
Acknowledgements
We would like to thank the reviewers for their thoughtful comments. The work is supported in part by the National Natural Science Foundation of China (Nos. 61872314 and 61802339), the Open Project Program of the State Key Lab of CADCG of Zhejiang University (No. A2001) and the Natural Science Foundation of Zhejiang Province, China (Nos. LY21F020029 and LY19F020011).
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Sun, L., Zhang, X., Pan, X. et al. Visual analytics of genealogy with attribute-enhanced topological clustering. J Vis 25, 361–377 (2022). https://doi.org/10.1007/s12650-021-00802-x
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DOI: https://doi.org/10.1007/s12650-021-00802-x