THE PROJECT DEVELOPS METHODS AND TOOLS FOR ANALYZING LARGE
  DATA SETS AND FOR SEARCHING FOR UNEXPECTED RELATIONSHIPS
  IN THE DATA. THE PROJECT COMBINES DEVELOPMENT OF
  COMBINATORIAL PATTERN MATCHING ALGORITHMS WITH STATISTICAL
  TECHNIQUES AND DATABASE METHODS. THE RESULTING TECHNIQUES
  TYPICALLY SEARCH THROUGH A LARGE COLLECTION OF POTENTIAL
  LOCAL MODELS THAT DESCRIBE SOME ASPECT OF THE DATA IN AN
  EASILY UNDERSTANDABLE WAY. THE PROJECT HAS ALSO STUDIED
  THE CONSTRUCTION OF EFFICIENT PREDICTORS FROM LARGE MASSES
  OF DATA.

  THE GROUP HAS PRODUCED SEVERAL IMPORTANT RESULTS IN
  METHODS FOR FINDING ASSOCIATION RULES, EPISODE RULES, AND
  SIMILARITIES FROM RELATIONAL DATABASES, EVENT SEQUENCE
  DATA, AND TEXT. THE METHODS HAVE SO FAR BEEN APPLIED IN
  TELECOMMUNICATIONS, PALEOECOLOGY, MEDICAL GENETICS AND
  TEXT DATABASES. THE DATA MINING RESEARCH HAS LOTS OF
  INDUSTRIAL APPLICATIONS, AND PART OF THE RESEARCH GROUP
  WORKS CURRENTLY IN INDUSTRY.

  DEVELOPING EFFICIENT, ANALYTICALLY WELL­MOTIVATED GENERAL
  PURPOSE LEARNING ALGORITHMS FOR DIFFERENT MACHINE LEARNING
  AND DATA MINING PURPOSES IS ONE OF OUR AIMS. ONE OF THE
  MAJOR GOALS FOR THE NEXT YEARS IS FURTHER INTEGRATION OF
  COMBINATORIAL AND STATISTICAL TECHNIQUES. THE PROJECT HAS
  HAD GOOD SUCCESS IN, E.G., APPROXIMATING JOINT
  DISTRIBUTIONS BY USING ASSOCIATION RULES AND MAXIMUM
  ENTROPY PRINCIPLES. SIMILAR COMBINATION TECHNIQUES CAN
  PROFITABLY BE USED ELSEWHERE, TOO: FOR EXAMPLE, ENSEMBLE
  METHODS IN COMBINATION WITH ASSOCIATION AND EPISODE RULES
  CAN PRODUCE SIMPLE BUT POWERFUL PREDICTORS. ANOTHER GOAL
  OF THE PROJECT ARE NOVEL METHODS FOR ANALYZING SPATIAL AND
  SPATIO­TEMPORAL DATA ARISING IN TELECOMMUNICATIONS AND
  BIOLOGICAL APPLICATIONS.