Flora republicae popularis Romanicae [Malvaceae -- Umbelliferae]. Bucuresti Vol. 6

The Collation Of Large Electronic Databases Of Scienti?c And Commercial Infor- Tion Has Led To A Dramatic Growth Of Interest In Methods For Discovering Struc- Res In Such Databases. These Methods Often Go Under The General Name Of Data Mining. One Important Subdiscipline Within Data Mining Is Concerned With The Identi?cation And Detection Of Anomalous, Interesting, Unusual, Or Valuable - Cords Or Groups Of Records, Which We Call Patterns. Familiar Examples Are The Detection Of Fraud In Credit-card Transactions, Of Particular Coincident Purchases In Supermarket Transactions, Of Important Nucleotide Sequences In Gene Sequence Analysis, And Of Characteristic Traces In Eeg Records. Tools For The Detection Of Such Patterns Have Been Developed Within The Data Mining Community, But Also Within Other Research Communities, Typically Without An Awareness That The - Sic Problem Was Common To Many Disciplines. This Is Not Unreasonable: Each Of These Disciplines Has A Large Literature Of Its Own, And A Literature Which Is Growing Rapidly. Keeping Up With Any One Of These Is Di?cult Enough, Let Alone Keeping Up With Others As Well, Which May In Any Case Be Couched In An - Familiar Technical Language. But, Of Course, This Means That Opportunities Are Being Lost, Discoveries Relating To The Common Problem Made In One Area Are Not Transferred To The Other Area, And Breakthroughs And Problem Solutions Are Being Rediscovered, Or Not Discovered For A Long Time, Meaning That E?ort Is Being Wasted And Opportunities May Be Lost. Pattern Detection And Discovery / David J. Hand -- Detecting Interesting Instances / Katharina Morik -- Complex Data: Mining Using Patterns / Arno Siebes And Zbyszek Struzik -- Determining Hit Rate In Pattern Search / Richard J. Bolton, David J. Hand, And Niall M. Adams -- An Unsupervised Algorithm For Segmenting Categorical Timeseries Into Episodes / Paul Cohen, Brent Heeringa, And Niall M. Adams -- If You Can't See The Pattern, Is It There? / Antony Unwin -- Dataset Filtering Techniques In Constraint-based Frequent Pattern Mining / Marek Wojciechowski And Maciej Zakrzewicz -- Concise Representations Of Association Rules / Marzena Kryszkiewicz -- Constraint-based Discovery And Inductive Queries / Baptiste Jeudy And Jean-franȯis Boulicaut -- Relational Association Rules: Getting Warmer / Bart Goethals And Jan Van Den Bussche -- Mining Text Data: Special Features And Patterns / M. Delgado [and Others] -- Modelling And Incorporating Background Knowledge In The Web Mining Process / Myra Spiliopoulou And Carsten Pohle -- Modeling Information In Textual Data Combining Labeled And Unlabeled Data / Dunja Mladenic -- Discovery Of Frequent Word Sequences In Text / Helena Ahonen-myka -- Pattern Detection And Discovery / Pierre-yves Rolland And Jean-gabriel Ganascia -- Discovery Of Core Episodes From Sequences / Frank Hoppner -- Patterns Of Dependencies In Dynamic Multivariate Data / Ursula Gather [and Others]. David J. Hand, Niall M. Adams, Richard J. Bolton (eds.). Includes Bibliographical References And Index.