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• W2601386321 abstract "Data mining methods seek to discover unexpected and interesting regularities, called patterns, in presented data sets. However, the methods often return a collection of patterns for any data set, even a random one. Statistical significance testing can be applied in these scenarios to select the surprising patterns that do not appear as clearly in random data. As each pattern is tested for significance, a set of statistical hypotheses are considered simultaneously. The multiple comparison of several hypotheses simultaneously is called multiple hypothesis testing, and special treatment is required to adequately control the probability of falsely declaring a pattern statistically significant. However, the traditional methods for multiple hypothesis testing can not be used in data mining scenarios, because these methods do not consider the problem of varying set of hypotheses, which is inherent in data mining.This thesis provides an introduction to the problem and reviews some published work on the subject. The focus is in multiple hypothesis testing and specifically in data mining. The problems with traditional multiple hypothesis testing methods in data mining scenarios are discussed, and a solution to these problems is presented. The solution uses randomization, which involves drawing samples of random data sets and using the data mining algorithm with them. The results on the random data sets are then compared with the results on the original data set. Randomization is introduced and discussed in general, and possible randomization schemes in different data mining scenarios are presented. The solution is applied in iterative data mining and biclustering scenarios. Experiments are carried out to display the utility in these applications.; Tiedonlouhinnan menetelmilla pyritaan loytamaan annetusta aineistosta yllattavia ja mielenkiintoisia saannonmukaisuuksia, joita kutsutaan hahmoiksi. Useat menetelmat kuitenkin loytavat hahmoja kaikista aineistoista, jopa taysin satunnaisista. Naissa tilanteissa voidaan kayttaa tilastollista testausta valitsemaan yllattavat hahmot, jotka eivat esiinny yhta vahvasti satunnaisessa aineistossa. Monen hahmon tilastollista merkittavyytta testatessa kasitellaan samalla yhdenaikaisesti joukkoa tilastollisia hypoteeseja. Usean hypoteesin yhdenaikaista testausta kutsutaan monen hypoteesin testaamiseksi, joka vaatii erityistoimenpiteita, jotta vaarien johtopaatosten todennakoisyytta voidaan hallita. Kuitenkaan tyypillisia monen hypoteesin testausmenetelmia ei voida kayttaa tiedonlouhinnassa, koska ne eivat ota huomioon tiedonlouhinnassa tyypillista vaihtelevan hypoteesijoukon ongelmaa.Tama vaitoskirja esittelee ongelman ja tarkastelee aiheeseen liittyvia julkaisuja. Kirja keskittyy monen hypoteesin testaamiseen erityisesti tiedonlouhinnan tilanteissa. Tyypillisten monen hypoteesin testaamiseen kaytettavien menetelmien ongelmia tiedonlouhinnassa kasitellaan, ja ongelmiin esitetaan ratkaisu. Tama perustuu satunnaistukseen, jossa luodaan satunnaisia aineistoja ja kaytetaan tiedonlouhinnan…" @default.
• W2601386321 created "2017-04-07" @default.
• W2601386321 creator A5056086594 @default.
• W2601386321 date "2012-01-01" @default.
• W2601386321 modified "2023-09-26" @default.
• W2601386321 title "Multiple hypothesis testing in data mining" @default.
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