{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:16:02Z","timestamp":1741666562199,"version":"3.38.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>In many clustering problems, the whole data is not always static. Over time, part of it is likely to be changed, such as updated, erased, etc. Suffer this effect, the timeline can be divided into multiple time segments. And, the data at each time slice is static. Then, the data along the timeline shows a series of dynamic intermediate states. The union set of data from all time slices is called the time-series data. Obviously, the traditional clustering process does not apply directly to the time-series data. Meanwhile, repeating the clustering process at every time slices costs tremendous. In this paper, we analyze the transition rules of the data set and cluster structure when the time slice shifts to the next. We find there is a distinct correlation of data set and succession of cluster structure between two adjacent ones, which means we can use it to reduce the cost of the whole clustering process. Inspired by it, we propose a dynamic density clustering method (DDC) for time-series data. In the simulations, we choose 6 representative problems to construct the time-series data for testing DDC. The results show DDC can get high accuracy results for all 6 problems while reducing the overall cost markedly.<\/jats:p>","DOI":"10.3233\/ida-205459","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T20:05:37Z","timestamp":1635883537000},"page":"1487-1506","source":"Crossref","is-referenced-by-count":0,"title":["Time-series data dynamic density clustering"],"prefix":"10.1177","volume":"25","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"},{"name":"Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}]},{"given":"Yu","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}]},{"given":"Yuekai","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, Shaanxi, China"}]},{"given":"Qing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Sport and Health Sciences, Xi\u2019an Physical Education University, Xi\u2019an, Shaanxi, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/IDA-205459_ref1","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","article-title":"A review on time series data mining","volume":"24","author":"Fu","year":"2011","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.3233\/IDA-205459_ref2","first-page":"112","article-title":"Time series spectral clustering analysis of taxi data","volume":"8","author":"Zhao","year":"2020","journal-title":"Bulletin of Surveying and Mapping"},{"key":"10.3233\/IDA-205459_ref3","first-page":"3677","article-title":"A time series dynamic clustering algorithm","volume":"10","author":"Xie","year":"2012","journal-title":"Application Research of Computers"},{"issue":"2","key":"10.3233\/IDA-205459_ref5","first-page":"49","article-title":"Review of time series semi-supervised classification","volume":"35","author":"Shan","year":"2018","journal-title":"Journal of the Hebei Academy of Sciences"},{"key":"10.3233\/IDA-205459_ref7","unstructured":"J.B. MacQueen, Some Methods for Classification and Analysis of Multivariate Observation, in: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281\u2013297."},{"issue":"6191","key":"10.3233\/IDA-205459_ref8","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"key":"10.3233\/IDA-205459_ref9","doi-asserted-by":"crossref","unstructured":"T. Zhang, R. Ramakrishnan and M. Livny, BIRCH: an efficient data clustering method for very large databases, in: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, 25(2) (1996), 103\u2013114.","DOI":"10.1145\/235968.233324"},{"key":"10.3233\/IDA-205459_ref10","unstructured":"M. Ester et al., A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226\u2013231."},{"issue":"10","key":"10.3233\/IDA-205459_ref11","first-page":"2186","article-title":"Self-adaptive local eps DBSCAN","volume":"39","author":"QIN","year":"2018","journal-title":"Journal of Chinese Computer Systems"},{"issue":"1","key":"10.3233\/IDA-205459_ref14","first-page":"97","article-title":"An improved k-means dynamic clustering algorithm","volume":"33","author":"Zhang","year":"2016","journal-title":"Journal of Chongqing Normal University (Natural Science)"},{"key":"10.3233\/IDA-205459_ref15","doi-asserted-by":"crossref","unstructured":"R. Agrawal, C. Faloutsos and A. Swami, Efficient similarity search in sequence databases, in: Proc of the 4th International Conference on Foundations of Data Organization and Algorithms, 1993, pp. 69\u201384.","DOI":"10.1007\/3-540-57301-1_5"},{"issue":"6","key":"10.3233\/IDA-205459_ref16","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.epsr.2016.05.023","article-title":"Dynamic clustering of residential electricity consumption time series data based on Hausdorff distance","volume":"140","author":"Ben\u00edtez","year":"2016","journal-title":"Electric Power Systems Research"},{"issue":"7","key":"10.3233\/IDA-205459_ref17","first-page":"1114","article-title":"Similarity dynamical clustering algorithm based on multidimensional shape features for time series","volume":"39","author":"Wang","year":"2017","journal-title":"Chinese Journal of Engineering"},{"issue":"12","key":"10.3233\/IDA-205459_ref18","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.eswa.2016.06.012","article-title":"Hierarchical clustering of time series data with parametric derivative dynamic time warping","volume":"62","author":"\u0141uczak","year":"2016","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.3233\/IDA-205459_ref19","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1007\/s00180-009-0178-4","article-title":"KmL: k-means for longitudinal data","volume":"25","author":"Genolini","year":"2016","journal-title":"Computational Statistics"},{"issue":"1","key":"10.3233\/IDA-205459_ref20","first-page":"53","article-title":"Clustering time series based on orthogonal function system","volume":"36","author":"Min","year":"2016","journal-title":"System Science and Mathematics"},{"key":"10.3233\/IDA-205459_ref21","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.neucom.2013.08.006","article-title":"Agreement-based fuzzy C-means for clustering data with blocks of features","volume":"127","author":"Izakian","year":"2014","journal-title":"Neurocomputing"},{"issue":"5","key":"10.3233\/IDA-205459_ref22","first-page":"33","article-title":"Clustering algorithm for time series based on locally extreme point","volume":"41","author":"Sun","year":"2015","journal-title":"Computer Engineering"},{"issue":"11","key":"10.3233\/IDA-205459_ref23","first-page":"1301","article-title":"Non-equal time series clustering algorithm with sliding window STS distance","volume":"9","author":"Liu","year":"2015","journal-title":"Journal of Frontiers of Computer Science and Technology"},{"issue":"3","key":"10.3233\/IDA-205459_ref24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.engappai.2018.03.013","article-title":"Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer","volume":"72","author":"Shukri","year":"2018","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.3233\/IDA-205459_ref26","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.knosys.2018.01.031","article-title":"Improved k-means algorithm based on density canopy","author":"Zhang","year":"2018","journal-title":"Knowledge Based Systems"},{"issue":"4","key":"10.3233\/IDA-205459_ref27","first-page":"1027","article-title":"Improved BIRCH clustering algorithm based on connectivity distance and intensity","volume":"39","author":"Fan","year":"2019","journal-title":"Journal of Computer Applications"},{"issue":"1","key":"10.3233\/IDA-205459_ref28","first-page":"93","article-title":"An improved adaptive and fast AF-DBSCAN clustering algorithm","volume":"11","author":"Zhou","year":"2016","journal-title":"CAAI Transactions on Intelligent Systems"},{"issue":"6","key":"10.3233\/IDA-205459_ref29","first-page":"1668","article-title":"Multi-density clustering algorithm DBSCAN based on region division","volume":"35","author":"Han","year":"2018","journal-title":"Application Research of Computers"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-205459","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T13:49:21Z","timestamp":1741614561000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-205459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":24,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/ida-205459","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"type":"print","value":"1088-467X"},{"type":"electronic","value":"1571-4128"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}