Page 164 - DCAP606_BUSINESS_INTELLIGENCE
P. 164

Unit 11: Data Mining




               Segmentation algorithms: This type of algorithm divides data into groups, or clusters, of  Notes
               items that have similar properties.

               Association algorithms: This type of algorithm finds correlations between different
               attributes in a dataset. The most common application of this kind of algorithm is for
               creating association rules, which can be used in a market analysis.
               Sequence analysis algorithms: This type summarize frequent sequences or episodes in
               data, such as a Web path flow.

          Self Assessment

          Fill in the blanks:

          14.  A ........................................... is a set of heuristics and calculations that creates a data mining
               model from data.

          15.  ............................................. type of algorithm finds correlations between different attributes
               in a dataset.




             Case Study  Logic-ITA Student Data


                      e have performed a number of queries on datasets collected by the Logic-ITA
                      to assist teaching and learning. The Logic-ITA is a web-based tool used at
             WSydney University since 2001, in a course taught by the second author. Its
            purpose is to help students practice logic formal proofs and to inform the teacher of the
            class progress.
            Context of Use
            Over the four years, around 860 students attended the course and used the tool, in which an
            exercise consists of a set of formulas (called premises) and another formula (called the
            conclusion). The aim is to prove that the conclusion can validly be derived from the
            premises. For this, the student has to construct new formulas, step by step, using logic
            rules and formulas previously established in the proof, until the conclusion is derived.
            There is no unique solution and any valid path is acceptable. Steps are checked on the fly
            and, if incorrect, an error message and possibly a tip are displayed. Students used the tool
            at their own discretion. A consequence is that there is neither a fixed number nor a fixed
            set of exercises done by all students.
            Data Stored

            The tool’s teacher module collates all the student models into a database that the teacher
            can query and mine. Two often queried tables of the database are the tables mistake and
            correct_step. The most common variables are shown in Table 1.





                                                                                Contd....






                                           LOVELY PROFESSIONAL UNIVERSITY                                   159
   159   160   161   162   163   164   165   166   167   168   169