乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T23:33:57Z","timestamp":1723073637662},"reference-count":41,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2010,8,25]],"date-time":"2010-08-25T00:00:00Z","timestamp":1282694400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Statistical Analysis"],"published-print":{"date-parts":[[2010,10]]},"abstract":"Abstract<\/jats:title>The goal of frequent subgraph mining is to detect subgraphs that frequently occur in a dataset of graphs. In classification settings, one is often interested in discovering discriminative<\/jats:italic> frequent subgraphs, whose presence or absence is indicative of the class membership of a graph. In this article, we propose an approach to feature selection on frequent subgraphs, called CORK<\/jats:italic>, that combines two central advantages. First, it optimizes a submodular quality criterion, which means that we can yield a near\u2010optimal solution using greedy feature selection. Second, our submodular quality function criterion can be integrated into gSpan, the state\u2010of\u2010the\u2010art tool for frequent subgraph mining, and help to prune the search space for discriminative frequent subgraphs even during<\/jats:italic> frequent subgraph mining. Copyright \u00a9 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 302\u2010318, 2010<\/jats:p>","DOI":"10.1002\/sam.10084","type":"journal-article","created":{"date-parts":[[2010,8,29]],"date-time":"2010-08-29T23:24:54Z","timestamp":1283124294000},"page":"302-318","source":"Crossref","is-referenced-by-count":31,"title":["Discriminative frequent subgraph mining with optimality guarantees"],"prefix":"10.1002","volume":"3","author":[{"given":"Marisa","family":"Thoma","sequence":"first","affiliation":[]},{"given":"Hong","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Arthur","family":"Gretton","sequence":"additional","affiliation":[]},{"given":"Jiawei","family":"Han","sequence":"additional","affiliation":[]},{"given":"Hans\u2010Peter","family":"Kriegel","sequence":"additional","affiliation":[]},{"given":"Alex","family":"Smola","sequence":"additional","affiliation":[]},{"given":"Le","family":"Song","sequence":"additional","affiliation":[]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xifeng","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Karsten M.","family":"Borgwardt","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2010,8,25]]},"reference":[{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/nrd1156"},{"key":"e_1_2_7_3_2","doi-asserted-by":"crossref","unstructured":"S.Kramer L.Raedt andC.Helma Molecular feature mining in HIV data In Proceedings of KDD San Francisco CA 2001 136\u2013143.","DOI":"10.1145\/502512.502533"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.127"},{"key":"e_1_2_7_5_2","doi-asserted-by":"crossref","unstructured":"H.Cheng X.Yan J.Han andC.Hsu Discriminative frequent pattern analysis for effective classification In Proceedings of ICDE Istanbul Turkey 2007;716\u2013725.","DOI":"10.1109\/ICDE.2007.367917"},{"key":"e_1_2_7_6_2","unstructured":"H.Kashima K.Tsuda andA.Inokuchi Marginalized kernels between labeled graphs In Proceedings of ICML Washington DC 2003;321\u2013328."},{"key":"e_1_2_7_7_2","doi-asserted-by":"crossref","unstructured":"N.WaleandG.Karypis Comparison of descriptor spaces for chemical compound retrieval and classification In Proceedings of ICDM Hong Kong 2006;678\u2013689.","DOI":"10.21236\/ADA444816"},{"key":"e_1_2_7_8_2","unstructured":"N.ShervashidzeandK. 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