Matches in SemOpenAlex for { <https://semopenalex.org/work/W1832728800> ?p ?o ?g. }
- W1832728800 endingPage "177" @default.
- W1832728800 startingPage "161" @default.
- W1832728800 abstract "Over the last decades, a great variety of data mining techniques have been developed to reach goals concerning Knowledge Discovery in Databases. Among them, cluster detection techniques are of major importance. Although these techniques have already been largely explored in the scientific literature, there are at least two important open issues: the existent algorithms are not scalable for large high‐dimensional datasets, and the unsupervised nature of traditional data clustering makes it very difficult to generate meaningful clusters. This article presents an overview of the strategies being explored in order to deal more deeply with these issues. Moreover, it describes a new semi‐supervised clustering strategy that exemplifies the integration of several approaches and that can be employed with partitioning algorithms, such as PAM and Clarans. The technique addresses an improvement to these types of algorithms, which is obtained by using must‐link feedback information provided by the users in an interactive and visual environment. WIREs Data Mining Knowl Discov 2014, 4:161–177. doi: 10.1002/widm.1127 This article is categorized under: Technologies > Structure Discovery and Clustering" @default.
- W1832728800 created "2016-06-24" @default.
- W1832728800 creator A5009825422 @default.
- W1832728800 creator A5014315681 @default.
- W1832728800 creator A5014783020 @default.
- W1832728800 creator A5014867996 @default.
- W1832728800 creator A5020611078 @default.
- W1832728800 date "2014-04-28" @default.
- W1832728800 modified "2023-09-30" @default.
- W1832728800 title "Open issues for partitioning clustering methods: an overview" @default.
- W1832728800 cites W1481265159 @default.
- W1832728800 cites W1482375083 @default.
- W1832728800 cites W1485451569 @default.
- W1832728800 cites W1499599687 @default.
- W1832728800 cites W1516407653 @default.
- W1832728800 cites W1577072181 @default.
- W1832728800 cites W1597285176 @default.
- W1832728800 cites W1636513602 @default.
- W1832728800 cites W1872311875 @default.
- W1832728800 cites W1965555277 @default.
- W1832728800 cites W1975331922 @default.
- W1832728800 cites W1976684966 @default.
- W1832728800 cites W1977556410 @default.
- W1832728800 cites W1983540749 @default.
- W1832728800 cites W1992419399 @default.
- W1832728800 cites W1992944354 @default.
- W1832728800 cites W2001280717 @default.
- W1832728800 cites W2011430131 @default.
- W1832728800 cites W2013136599 @default.
- W1832728800 cites W2016441722 @default.
- W1832728800 cites W2020271151 @default.
- W1832728800 cites W2022056881 @default.
- W1832728800 cites W2029586025 @default.
- W1832728800 cites W2033665526 @default.
- W1832728800 cites W2034616054 @default.
- W1832728800 cites W2034958663 @default.
- W1832728800 cites W2036477303 @default.
- W1832728800 cites W2038044292 @default.
- W1832728800 cites W2038678505 @default.
- W1832728800 cites W2043144130 @default.
- W1832728800 cites W2044728660 @default.
- W1832728800 cites W2045831561 @default.
- W1832728800 cites W2046589280 @default.
- W1832728800 cites W2049797265 @default.
- W1832728800 cites W2054274648 @default.
- W1832728800 cites W2079361215 @default.
- W1832728800 cites W2079810998 @default.
- W1832728800 cites W2082745988 @default.
- W1832728800 cites W2087743374 @default.
- W1832728800 cites W2095705137 @default.
- W1832728800 cites W2098284677 @default.
- W1832728800 cites W2102831150 @default.
- W1832728800 cites W2107667794 @default.
- W1832728800 cites W2109076057 @default.
- W1832728800 cites W2110889574 @default.
- W1832728800 cites W2112751990 @default.
- W1832728800 cites W2113180829 @default.
- W1832728800 cites W2119111481 @default.
- W1832728800 cites W2125775272 @default.
- W1832728800 cites W2139956879 @default.
- W1832728800 cites W2140366654 @default.
- W1832728800 cites W2143428259 @default.
- W1832728800 cites W2145260450 @default.
- W1832728800 cites W2145862222 @default.
- W1832728800 cites W2146570986 @default.
- W1832728800 cites W2151530263 @default.
- W1832728800 cites W2151749331 @default.
- W1832728800 cites W2153233077 @default.
- W1832728800 cites W2153839362 @default.
- W1832728800 cites W2157530472 @default.
- W1832728800 cites W2158703410 @default.
- W1832728800 cites W2163952039 @default.
- W1832728800 cites W2164456230 @default.
- W1832728800 cites W2165533158 @default.
- W1832728800 cites W2406841297 @default.
- W1832728800 cites W2618280608 @default.
- W1832728800 cites W2949383309 @default.
- W1832728800 cites W2997027240 @default.
- W1832728800 cites W299839057 @default.
- W1832728800 cites W4205606066 @default.
- W1832728800 cites W4231029117 @default.
- W1832728800 cites W4241938281 @default.
- W1832728800 cites W4246354968 @default.
- W1832728800 cites W4253133244 @default.
- W1832728800 doi "https://doi.org/10.1002/widm.1127" @default.
- W1832728800 hasPublicationYear "2014" @default.
- W1832728800 type Work @default.
- W1832728800 sameAs 1832728800 @default.
- W1832728800 citedByCount "10" @default.
- W1832728800 countsByYear W18327288002015 @default.
- W1832728800 countsByYear W18327288002017 @default.
- W1832728800 countsByYear W18327288002018 @default.
- W1832728800 countsByYear W18327288002019 @default.
- W1832728800 countsByYear W18327288002021 @default.
- W1832728800 countsByYear W18327288002023 @default.
- W1832728800 crossrefType "journal-article" @default.
- W1832728800 hasAuthorship W1832728800A5009825422 @default.
- W1832728800 hasAuthorship W1832728800A5014315681 @default.