Undergraduate research experiences in data mining

ABSTRACT

The new interdisciplinary field of Data Mining emerged in the early 1990s as a response to the profusion of digital data generated in numerous fields such as biology, chemistry, astronomy, advertising, banking and finance, retail market, stock market, and the WWW. In this paper, I describe an undergraduate course in Data Mining offered at the College of Saint Benedict and Saint John's University in Spring of 2007 as a CSCI-317-upper-division "Topics in Computer Science"- course, entitled "Data Intelligence." One of the main objectives of the course was to engage students in experimental computing research through a number of carefully planned research activities resulting in better understanding of the course contents and deeper insights into the challenges faced by the data mining community.

REFERENCES

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• 3: Jiawei Han , Jian Pei , Yiwen Yin, Mining frequent patterns without candidate generation, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, p.1-12, May 15-18, 2000, Dallas, Texas, United States
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INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.2 Computer and Information Science Education
          Subjects: Computer science education

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Concept learning; Analogies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms; Similarity measures

General Terms:
Algorithms, Design, Experimentation, Measurement, Performance

Keywords:
data mining, machine learning, pattern recognition, undergraduate course design