{"type":"ScholarlyArticle","title":"Het2Hom: Representation of Heterogeneous Attributes into Homogeneous Concept Spaces for Categorical-and-Numerical-Attribute Data Clustering","authors":[{"name":"Yiqun Zhang","slug":"yiqun-zhang","given":"Yiqun","family":"Zhang","affiliation":"School of Computer Science and Technology , Guangdong University of Technology , Guangzhou , China"},{"name":"Yiu-ming Cheung","slug":"yiu-ming-cheung","given":"Yiu-ming","family":"Cheung","affiliation":"Department of Computer Science , Hong Kong Baptist University , Hong Kong , China"},{"name":"An Zeng","slug":"an-zeng","given":"An","family":"Zeng","affiliation":"School of Computer Science and Technology , Guangdong University of Technology , Guangzhou , China"}],"abstract":"Data sets composed of a mixture of categorical and numerical attributes (also called mixed data hereinafter) are common in real-world cluster analysis. However, insightful analysis of such data under an unsupervised scenario using clustering is extremely challenging because the information provided by the two different types of attributes is heterogeneous, being at different concept hierarchies. That is, the values of a categorical attribute represent a set of different concepts (e.g., professor, lawyer, and doctor of the attribute \"occupation\"), while the values of a numerical attribute describe the tendencies toward two different concepts (e.g., low and high of the attribute \"income\"). To appropriately use such heterogeneous information in clustering, this paper therefore proposes a novel attribute representation learning method called Het2Hom, which first converts the heterogeneous attributes into a homogeneous form, and then learns attribute representations and data partitions on such a homogeneous basis. Het2Hom features low time complexity and intuitive interpretability. Extensive experiments show that Het2Hom outperforms the state-of-the-art counterparts.","doi":"10.24963/ijcai.2022/522","url":"https://ijcai.org/proceedings/2022/522","pdf":"https://ijcai.org/proceedings/2022/0522.pdf","markdown":"https://ijcai.org/proceedings/2022/522.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/522","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":522,"pages":{"first":3758,"last":3765},"keywords":["Machine Learning: Clustering"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986826","length_sec":93,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984969","length_sec":312,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Agresti, 2003] Alan Agresti. Categorical data analysis. John Wiley & Sons, 2003.","title":"Categorical data analysis","authors":"Alan Agresti","year":"2003","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ahmad and Dey, 2007] Amir Ahmad and Lipika Dey. A method to compute distance between two categorical val- ues of same attribute in unsupervised learning for categor- ical data set. Pattern Recognition Letters, 28(1):110-118, 2007.","title":"A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set","authors":"Ahmad, Dey ;, Amir Ahmad, Lipika Dey","year":"2007","venue":"Pattern Recognition Letters","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ball and Hall, 1967] Geoffrey H Ball and David J Hall. A clustering technique for summarizing multivariate data. Behavioral Science, 12(2):153-155, 1967.","title":"A clustering technique for summarizing multivariate data","authors":"H Geoffrey, David J Ball, Hall","year":"1967","venue":"Behavioral Science","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Boriah et al., 2008] Shyam Boriah, Varun Chandola, and Vipin Kumar. Similarity measures for categorical data: A comparative evaluation. In Proceedings of the 2008 SIAM International Conference on Data Mining, pages 243-254, 2008.","title":"Similarity measures for categorical data: A comparative evaluation","authors":"Shyam Boriah, Varun Chandola, Vipin Kumar","year":"2008","venue":"Proceedings of the 2008 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cheung and Jia, 2013] Yiu-ming Cheung and Hong Jia. Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number. Pattern Recognition, 46(8):2228-2238, 2013.","title":"Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number","authors":"Yiu-Ming Cheung, Hong Jia","year":"2013","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Demšar, 2006] Janez Demšar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7(1):1-30, 2006.","title":"Janez Demšar. Statistical comparisons of classifiers over multiple data sets","authors":"Demšar","year":"2006","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[dos Santos and Zárate, 2015] Tiago RL dos Santos and Luis E Zárate. Categorical data clustering: What similarity measure to recommend? Expert Systems with Application- s, 42(3):1247-1260, 2015.","title":"Categorical data clustering: What similarity measure to recommend?","authors":"R L Tiago, Santos, Luis E Zárate","year":"2015","venue":"Expert Systems with Applications","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Estévez et al., 2009] Pablo A Estévez, Michel Tesmer, Claudio A Perez, and Jacek M Zurada. Normalized mu- tual information feature selection. IEEE Transactions on Neural Networks, 20(2):189-201, 2009.","title":"Normalized mutual information feature selection","authors":"Pablo A Estévez, Michel Tesmer, Claudio A Perez, Jacek M Zurada","year":"2009","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gates and Ahn, 2017] Alexander J Gates and Yong-Yeol Ahn. The impact of random models on clustering sim- ilarity. The Journal of Machine Learning Research, 18(1):3049-3076, 2017.","title":"The impact of random models on clustering similarity","authors":"J Alexander, Yong-Yeol Gates, Ahn","year":"2017","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Goodall, 1966] David W Goodall. A new similarity index based on probability. Biometrics, pages 882-907, 1966.","title":"A new similarity index based on probability","authors":"David W Goodall","year":"1966","venue":"Biometrics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[He et al., 2005] Xiaofei He, Deng Cai, and Partha Niyogi. Laplacian score for feature selection. In Proceedings of the 18th International Conference on Neural Information Processing Systems, pages 507-514, 2005.","title":"Laplacian score for feature selection","authors":"Xiaofei He, Deng Cai, Partha Niyogi","year":"2005","venue":"Proceedings of the 18th International Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Huang, 1997] Zhexue Huang. Clustering large data sets with mixed numeric and categorical values. In Proceed- ings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 21-34, 1997.","title":"Clustering large data sets with mixed numeric and categorical values","authors":"Zhexue Huang, Huang","year":"1997","venue":"Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang, 1998] Zhexue Huang. Extensions to the k-means al- gorithm for clustering large data sets with categorical val- ues. Data Mining and Knowledge Discovery, 2(3):283- 304, 1998.","title":"Extensions to the k-means algorithm for clustering large data sets with categorical values","authors":"Zhexue Huang, Huang","year":"1998","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ienco et al., 2012] Dino Ienco, Ruggero G Pensa, and Rosa Meo. From context to distance: Learning dissimilarity for categorical data clustering. ACM Transactions on Knowl- edge Discovery from Data, 6(1):1-25, 2012.","title":"From context to distance: Learning dissimilarity for categorical data clustering","authors":"Dino Ienco, G Ruggero, Rosa Pensa, Meo","year":"2012","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Jia et al., 2016] Hong Jia, Yiu-ming Cheung, and Jiming Li- u. A new distance metric for unsupervised learning of cat- egorical data. IEEE Transactions on Neural Networks and Learning Systems, 27(5):1065-1079, 2016.","title":"A new distance metric for unsupervised learning of categorical data","authors":"Jia","year":"2016","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jian et al., 2017] Songlei Jian, Longbing Cao, Guansong Pang, Kai Lu, and Hang Gao. Embedding-based represen- tation of categorical data by hierarchical value coupling learning. In Proceedings of the 26th International Join- t Conference on Artificial Intelligence, pages 1937-1943, 2017.","title":"Embedding-based representation of categorical data by hierarchical value coupling learning","authors":"Jian","year":"2017","venue":"Proceedings of the 26th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/269"},{"position":17,"raw":"[Jian et al., 2018a] Songlei Jian, Longbing Cao, Kai Lu, and Hang Gao. Unsupervised coupled metric similarity for non-iid categorical data. IEEE Transactions on Knowledge and Data Engineering, 30(9):1810-1823, 2018.","title":"Unsupervised coupled metric similarity for non-iid categorical data","authors":"Jian","year":"2018","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Jian et al., 2018b] Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu, and Hang Gao. Cure: Flexible categori- cal data representation by hierarchical coupling learning. IEEE Transactions on Knowledge and Data Engineering, 31(5):853-866, 2018.","title":"Cure: Flexible categorical data representation by hierarchical coupling learning","authors":"Jian","year":"2018","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Kacem et al., 2015] Mohamed Aymen Ben Haj Kacem, Chiheb-Eddine Ben N'cir, and Nadia Essoussi. Mapreduce-based k-prototypes clustering method for big data. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, pages 1-7. IEEE, 2015.","title":"Mapreduce-based k-prototypes clustering method for big data","authors":"Mohamed Aymen, Ben Haj Kacem, Chiheb-Eddine Ben N'cir, Nadia Essoussi","year":"2015","venue":"Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Le and Ho, 2005] Si Quang Le and Tu Bao Ho. An association-based dissimilarity measure for categorical da- ta. Pattern Recognition Letters, 26(16):2549-2557, 2005.","title":"An association-based dissimilarity measure for categorical data","authors":"Si Quang, Le, Tu Bao Ho","year":"2005","venue":"Pattern Recognition Letters","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lin, 1998] Dekang Lin. An information-theoretic defini- tion of similarity. In Proceedings of the 15th International Conference on Machine Learning, pages 296-304, 1998.","title":"An information-theoretic definition of similarity","authors":"Dekang Lin","year":"1998","venue":"Proceedings of the 15th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(11):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton","authors":"Hinton Maaten","year":"2008","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Qian et al., 2015] Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, and Chuangyin Dang. Space structure and cluster- ing of categorical data. IEEE Transactions on Neural Net- works and Learning Systems, 27(10):2047-2059, 2015.","title":"Space structure and clustering of categorical data","authors":"Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, Chuangyin Dang","year":"2015","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xu and Wunsch, 2005] Rui Xu and Donald Wunsch. Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3):645-678, 2005.","title":"Survey of clustering algorithms","authors":"Rui Xu, Donald Wunsch","year":"2005","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhang and Cheung, 2018] Yiqun Zhang and Yiu-ming Che- ung. Exploiting order information embedded in ordered categories for ordinal data clustering. In Proceedings of the 24th International Symposium on Methodologies for Intelligent Systems, pages 247-257, 2018.","title":"Exploiting order information embedded in ordered categories for ordinal data clustering","authors":"Yiqun Zhang, Yiu-Ming Cheung","year":"2018","venue":"Proceedings of the 24th International Symposium on Methodologies for Intelligent Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhang and Cheung, 2020] Yiqun Zhang and Yiu-ming Che- ung. An ordinal data clustering algorithm with automated distance learning. In Proceedings of the 34th AAAI Con- ference on Artificial Intelligence, pages 6869-6876, 2020.","title":"An ordinal data clustering algorithm with automated distance learning","authors":"Yiqun Zhang, Yiu-Ming Cheung","year":"2020","venue":"Proceedings of the 34th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang and Cheung, 2021] Yiqun Zhang and Yiu-ming Che- ung. Learnable weighting of intra-attribute distances for categorical data clustering with nominal and ordi- nal attributes. IEEE Transactions on Pattern Analy- sis and Machine Intelligence, page DOI: 10.1109/TPA- MI.2021.3056510, 2021.","title":"Learnable weighting of intra-attribute distances for categorical data clustering with nominal and ordinal attributes","authors":"Yiqun Zhang, Yiu-Ming Cheung","year":"2021","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":"10.1109/tpa-mi.2021.3056510","url":null,"internal_url":null},{"position":28,"raw":"[Zhang and Cheung, 2022] Yiqun Zhang and Yiu-ming Che- ung. A new distance metric exploiting heteroge- neous inter-attribute relationship for ordinal-and-nominal- attribute data clustering. IEEE Transactions on Cybernet- ics, 52(2):758-771, 2022.","title":"A new distance metric exploiting heterogeneous inter-attribute relationship for ordinal-and-nominalattribute data clustering","authors":"Yiqun Zhang, Yiu-Ming Cheung","year":"2022","venue":"IEEE Transactions on Cybernetics","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2020] Yiqun Zhang, Yiu-ming Cheung, and Kaychen Tan. A unified entropy-based distance metric for ordinal-and-nominal-attribute data clustering. IEEE Transactions on Neural Networks and Learning Systems, 31(1):39-52, 2020.","title":"A unified entropy-based distance metric for ordinal-and-nominal-attribute data clustering","authors":"Yiqun Zhang, Yiu-Ming Cheung, Kaychen Tan","year":"2020","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Zhu et al., 2020] Chengzhang Zhu, Qi Zhang, Longbing Cao, and Arman Abrahamyan. Mix2vec: Unsupervised mixed data representation. In Proceedings of the 7th Inter- national Conference on Data Science and Advanced Ana- lytics, pages 118-127, 2020.","title":"Mix2vec: Unsupervised mixed data representation","authors":"Chengzhang Zhu, Qi Zhang, Longbing Cao, Arman Abrahamyan","year":"2020","venue":"Proceedings of the 7th International Conference on Data Science and Advanced Analytics","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zhu et al., 2022] Chengzhang Zhu, Longbing Cao, and Jianping Yin. Unsupervised heterogeneous coupling learn- ing for categorical representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(1):553- 549, 2022.","title":"Unsupervised heterogeneous coupling learning for categorical representation","authors":"Zhu","year":"2022","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}