KNN-kernel based clustering for spatio-temporal database
Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not...
Gespeichert in:
| Veröffentlicht in: | 2010 International Conference on Computer and Communication Engineering S. 1 - 6 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.05.2010
|
| Schlagworte: | |
| ISBN: | 9781424462339, 1424462339 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining. Moreover, the data in spatial temporal database can be categorized as high-dimensional data. Current density-based clustering might have difficulties with complex data sets including high-dimensional data. This paper presents Iterative Local Gaussian Clustering (ILGC), an algorithm that combines K-nearest neighbour (KNN) density estimation and Kernel density estimation, to cluster the spatiotemporal data. In this approach, the KNN density estimation is extended and combined with Kernel function, where KNN contributes in determining the best local data iteratively for kernel density estimation. The local best is defined as the set of neighbour data that maximizes the kernel function. Bayesian rule is used to deal with the problem of selecting the best local data. This paper utilized Gaussian kernel which has been proven successful in the clustering. To validate the KNN-kernel based algorithm, we compare its performance againts other popular algorithms, such as Self Organizing Maps (SOM) and K-Means, on Crime database. Results show that KNN-kernel based clustering has outperformed others. |
|---|---|
| AbstractList | Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining. Moreover, the data in spatial temporal database can be categorized as high-dimensional data. Current density-based clustering might have difficulties with complex data sets including high-dimensional data. This paper presents Iterative Local Gaussian Clustering (ILGC), an algorithm that combines K-nearest neighbour (KNN) density estimation and Kernel density estimation, to cluster the spatiotemporal data. In this approach, the KNN density estimation is extended and combined with Kernel function, where KNN contributes in determining the best local data iteratively for kernel density estimation. The local best is defined as the set of neighbour data that maximizes the kernel function. Bayesian rule is used to deal with the problem of selecting the best local data. This paper utilized Gaussian kernel which has been proven successful in the clustering. To validate the KNN-kernel based algorithm, we compare its performance againts other popular algorithms, such as Self Organizing Maps (SOM) and K-Means, on Crime database. Results show that KNN-kernel based clustering has outperformed others. |
| Author | Musdholifah, Aina Hashim, Siti Zaiton Bt Mohd Wasito, Ito |
| Author_xml | – sequence: 1 givenname: Aina surname: Musdholifah fullname: Musdholifah, Aina email: non.nana@gmail.com organization: Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai, Malaysia – sequence: 2 givenname: Siti Zaiton Bt Mohd surname: Hashim fullname: Hashim, Siti Zaiton Bt Mohd email: sitizaiton@utm.my organization: Fac. of Comput. Sci. & Inf. Syst., Univ. Teknol. Malaysia, Skudai, Malaysia – sequence: 3 givenname: Ito surname: Wasito fullname: Wasito, Ito organization: Fac. of Comput. Sci., Univ. of Indonesia, Jakarta, Indonesia |
| BookMark | eNo1j8tOwzAURI0ACVryA7DJD6T4nXiJrFIqqrLpvrqOr1EgTSLbLPh7giizOTrSaKRZkKthHJCQe0ZXjFHzuLXWrleczq6U0g1VF2TBJJdSc6HUJSlM3fy7MDekSOmDzpGKc0FvSfO631efGAfsSwcJfdn2Xylj7Ib3MoyxTBPkbqwynqYxQl96yPBbvCPXAfqExZlLcnheH-xLtXvbbO3TruoMzZX26JAF4z2TnoYmCAYGwbQza-e05MEwTV1rAGRLDXjvHDgvhHN1zaVYkoe_2Q4Rj1PsThC_j-ev4gcZjkqW |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICCCE.2010.5556805 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 1424462355 9781424462346 1424462347 9781424462353 |
| EndPage | 6 |
| ExternalDocumentID | 5556805 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ADFMO ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK IEGSK IERZE OCL RIE RIL |
| ID | FETCH-LOGICAL-i90t-6debe1f9dd14d0f8f31a9ea9c31a7bb642f9160bc9aa4c09addbbabd33bb77243 |
| IEDL.DBID | RIE |
| ISBN | 9781424462339 1424462339 |
| IngestDate | Wed Aug 27 02:53:13 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i90t-6debe1f9dd14d0f8f31a9ea9c31a7bb642f9160bc9aa4c09addbbabd33bb77243 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_5556805 |
| PublicationCentury | 2000 |
| PublicationDate | 2010-May |
| PublicationDateYYYYMMDD | 2010-05-01 |
| PublicationDate_xml | – month: 05 year: 2010 text: 2010-May |
| PublicationDecade | 2010 |
| PublicationTitle | 2010 International Conference on Computer and Communication Engineering |
| PublicationTitleAbbrev | ICCCE |
| PublicationYear | 2010 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0000452230 |
| Score | 1.4631033 |
| Snippet | Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Bayesian methods Bayesian rule Clustering algorithms Data mining Estimation Gaussian Kernel Indexes Iterative Local Gaussian Clustering Kernel Kernel clustering KNN Spatial databases Spatio-temporal database |
| Title | KNN-kernel based clustering for spatio-temporal database |
| URI | https://ieeexplore.ieee.org/document/5556805 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07a8MwEBZp6NAufSSlbzR0rBopVmxpNgktBZMhQ7bg0wNCg1Py-v09KXbaQpdOehlj60DfSbrvO0KeUi256WvDvISQwswCAxAlQ8_EyxS4F97EZBNZUajpVI9b5PnAhXHOxeAz9xKq8S7fLs02HJX1BkEuKwiWHmVZuudqHc5TojR4whvuFqJ6ohtJp6bdkGa47r3leT7cR3bVb_2VXiWiy-jsf991TrrfND06PgDQBWm56pKc_lAY7BD1XhTsw60qt6ABsCw1i23QRsBRiv4qXceIalYrVC1oiBgND3bJZDSc5K-sTpbA5ppvWGrRGsJra4W03CufiFK7UhssMwDcZXh0BDkYXZbScI3LGkAJNkkA0MGWyRVpV8vKXRPa7zvsTpWRaK6B8gr328pnoKxBMNfihnTCFMw-93IYs_rvb__uviMnzYU7F_ekvVlt3QM5NrvNfL16jDb8Alemmhs |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEJ4QNFEvPsD4dg8eXdnSpeyeGwgEbDhw4Ea6r4RIquHh73d2aVETL57abjebtpP0m5md7xuAp0RypttSU8eVb2FmFFUqyil6Jo4nirnI6dBsoptlYjaTkxo877kw1tpQfGZf_GnYyzfveutTZa2Ol8vygqUHHY5xz46ttc-oBHHwmFXsLcT1WFaiTtV1RZthsjVM07S3q-0q1_3VYCXgS__0f092Bs1voh6Z7CHoHGq2uICTHxqDDRCjLKNvdlXYJfGQZYhebr06At4l6LGSdaippqVG1ZL4mlE_sQnTfm-aDmjZLoEuJNvQxKA9IieNibhhTrg4yqXNpcZjVymMMxy6gkxpmedcM4k_NqVyZeJYKXSxeXwJ9eK9sFdA2m2Lw4nQHA3WEU5gxC1cVwmjEc5ldA0N_wnmHztBjHn59jd_Dz_C0WD6Op6Ph9noFo6r7XcW3UF9s9raezjUn5vFevUQ7PkFkLKdYg |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2010+International+Conference+on+Computer+and+Communication+Engineering&rft.atitle=KNN-kernel+based+clustering+for+spatio-temporal+database&rft.au=Musdholifah%2C+Aina&rft.au=Hashim%2C+Siti+Zaiton+Bt+Mohd&rft.au=Wasito%2C+Ito&rft.date=2010-05-01&rft.pub=IEEE&rft.isbn=9781424462339&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICCCE.2010.5556805&rft.externalDocID=5556805 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781424462339/lc.gif&client=summon&freeimage=true |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781424462339/mc.gif&client=summon&freeimage=true |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9781424462339/sc.gif&client=summon&freeimage=true |