Improved K‐means algorithm for clustering non‐spherical data
As one of the commonly used data mining algorithms, K‐means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non‐spherical data. An improved K‐means algorithm (IK‐means) is proposed to enhance clustering efficiency for non‐spherical data. T...
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| Published in: | Expert systems Vol. 39; no. 9 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Oxford
Blackwell Publishing Ltd
01.11.2022
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| ISSN: | 0266-4720, 1468-0394 |
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| Abstract | As one of the commonly used data mining algorithms, K‐means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non‐spherical data. An improved K‐means algorithm (IK‐means) is proposed to enhance clustering efficiency for non‐spherical data. The original dataset is clustered into a relatively larger number of high‐density sub‐clusters, and the final result is obtained by merging connected sub‐clusters respectively. The connectivity among sub‐clusters is evaluated by the sub‐clusters density and the nearest distance class between sub‐clusters. By testing on University of California, Irvine(UCI) datasets and several other artificial simulation datasets, the comparison of proposed IK‐means algorithm against DBSCAN, KGFCM shows its clustering capability for data of arbitrary shape. The clustering Adjusted Rand Index (ARI) value for 72,000 sizes data is 24% higher than DBSCAN, and 95.2% higher than KGFCM. For larger datasets, the IK‐means algorithm is faster than DBSCAN and KGFCM. |
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| AbstractList | As one of the commonly used data mining algorithms, K‐means has the advantage of fast clustering speed, but the disadvantage is that it is less effective for clustering non‐spherical data. An improved K‐means algorithm (IK‐means) is proposed to enhance clustering efficiency for non‐spherical data. The original dataset is clustered into a relatively larger number of high‐density sub‐clusters, and the final result is obtained by merging connected sub‐clusters respectively. The connectivity among sub‐clusters is evaluated by the sub‐clusters density and the nearest distance class between sub‐clusters. By testing on University of California, Irvine(UCI) datasets and several other artificial simulation datasets, the comparison of proposed IK‐means algorithm against DBSCAN, KGFCM shows its clustering capability for data of arbitrary shape. The clustering Adjusted Rand Index (ARI) value for 72,000 sizes data is 24% higher than DBSCAN, and 95.2% higher than KGFCM. For larger datasets, the IK‐means algorithm is faster than DBSCAN and KGFCM. |
| Author | He, Honglei Wang, Fang Zhu, Wenming He, Yuxuan |
| Author_xml | – sequence: 1 givenname: Honglei orcidid: 0000-0001-7310-3586 surname: He fullname: He, Honglei organization: Lianyungang Technical College – sequence: 2 givenname: Yuxuan orcidid: 0000-0003-3113-7184 surname: He fullname: He, Yuxuan organization: Xuzhou University of Technology – sequence: 3 givenname: Fang surname: Wang fullname: Wang, Fang organization: Lianyungang TCM Branch of Jiangsu Union Technical Institute – sequence: 4 givenname: Wenming orcidid: 0000-0002-8127-4251 surname: Zhu fullname: Zhu, Wenming email: rayman_zhu@163.com organization: Shenzhen Institute of Information Technology |
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| Cites_doi | 10.3390/s21051892 10.1142/S0218001419500125 10.1109/ICAICA50127.2020.9182394 10.1109/CloudCom.2013.89 10.1109/ACCESS.2020.3025193 10.1109/TCE.2009.5373781 10.1007/s11704-013-3158-3 10.1109/ICDMW.2017.12 10.1109/TCYB.2017.270234.w 10.1016/j.physa.2018.09.002 10.1109/IS3C.2012.166 10.1109/CLUSTER.2019.8891020 10.1109/IMSNA.2013.6743470 10.1109/2.781637 10.1109/TNN.2002.1000150 10.1109/ICCKE.2016.7802150 10.1109/TWC.2015.2467394 10.1109/ACCESS.2020.2988796 10.1109/TIP.2018.2796860 10.1145/3007748.3007773 10.2174/2213275912666190716121431 10.1109/LGRS.2016.2550666 10.3724/SP.J.1001.2008.01683 10.1109/NGCT.2015.7375201 10.1109/ISRITI.2018.8864459 10.1109/ICCECE51280.2021.9342102 10.1007/s00357-010-9049-5 10.1109/ICMSS.2009.5305409 10.1016/j.patcog.2016.03.008 10.1109/ICSESS49938.2020.9237746 10.1007/s10115-009-0216-0 10.1109/TKDE.2007.44 10.26599/TST.2019.9010078 10.1109/ICAPR.2017.8593078 10.1109/TCYB.2018.2868742 |
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| Title | Improved K‐means algorithm for clustering non‐spherical data |
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