学会員メーリングリストアーカイブ (2004年)

講演会のご案内 10月7日 Mi ning Interesting Patterns from Large Data Sets


ACM SIGMOD日本支部の皆様
日本データベース学会の皆様、


        講演会のご案内
       主催 ACM SIGMOD日本支部
       協賛 日本データベース学会

日時 10月7日(木) 午後5時〜午後6時30分
場所 東京大学生産技術研究所 E棟5階 会議室A(Ew-501)
   http://www.iis.u-tokyo.ac.jp/map/index.html

Title: Mining Interesting Patterns from Large Data Sets and their use in
       building Efficient Classifiers
Speaker: Professor Ramamohanarao Kotagiri
         Department of Computer Science and Software Engineering
         The University of Melbourne

参加費 無料

参加ご希望の方は、SIGMODホームページにて
     ( http://www.sigmodj.org )
会員登録の後(会費無料、すでに登録されている方は結構です)、
      sigmodj_lecture [at] tkl.iis.u-tokyo.ac.jpに
添付の参加申込書をお送り下さい。

皆様のご参加をお待ちしております。

                        ACM SIGMOD日本支部 支部長 北川博之
                       担当幹事 中野 美由紀

                        連絡(問合せ)先 ACM SIGMOD日本支部
                                sigmodj_lecture [at] tkl.iis.u-tokyo.ac.jp
                http://www.sigmodj.org

-----------------------------------------------------------------
To: sigmodj_lecture [at] tkl.iis.u-tokyo.ac.jp

ACM SIGMOD日本支部 講演会 参加申し込み

10月7日の講演会に参加
・名前
・ご所属
------------------------------------------------------------------


Title: Mining Interesting Patterns from Large Data Sets and their use in
       building Efficient Classifiers
Speaker: Professor Ramamohanarao Kotagiri
         Department of Computer Science and Software Engineering
         The University of Melbourne

Abstract:
Collection of vast amounts of information became feasible for many
businesses, organisations and institutions due to the availability of
inexpensive storage, communication and computer systems.  In practice,
the collected information could be several hundreds of terabytes.
However, collection of such a large volume of information is worthless
for these organisations unless the data can be used for inferring some
useful knowledge that can be employed for improving the efficiency of
their systems and/or processes. In my talk I introduce the notion of
Interesting Patterns from data sets, which can be used for understanding
existing processes, or building efficient classifiers and intelligent
decision systems.  Discovering these patterns is, however,
computationally a very hard problem in general. I will present efficient
ways of discovering some of these patterns, so that they can be used for
building intelligent decision systems.




-----------------------------------------------------------------------
中野 美由紀		東京大学 生産技術研究所 喜連川研究室
Miyuki NAKANO		Institute of Industrial Science, Univ. of Tokyo
miyuki [at] tkl.iis.u-tokyo.ac.jp