Browsing by Author "Kifle, Woldekidan"

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    Application of KDD on Crime Data to Support the . Advocacy and Awareness Raising Program of Forum on Street Children Ethiopia
    (Addis Ababa University, 2003-07) Kifle, Woldekidan; Kebede, Gashaw (PhD); Anagaw, Shegaw (PhD)
    T his thesis work gives an account of the process followed to determine the application of KDD to support the and advocacy and awareness raising program of FSCE and Addis Ababa Police Commission, and the potential of a data min in g learning scheme to discover regularities that underlie the crime dataset. T he KDD process as described by Fayyad , Piatetsky-Shapiro and Gregory ( 1996) that consists of five major phases , namely understanding of the problem domain , data se lection , data processing, data mining , and discuss ion and interpretation was adopted . T he discovery task was run on the crime database that consist s of 10,87 8 records/tuples in 17 tables describing a total of 25 attributes. Association rule mining, an exploratory data mining technique was applied to accomplish the goal of the research . T o this effect , the Aprion algorithm , which is an implementation of the Association rule in the Weka soft ware, was used. The KDD process can be applied on the crime database to good effect since it can result ill rulest hat can serve as input for the advocacy and awareness raising program . on the basis of subjective (opinions of domain experts) and objective ( support and confidence) measures of interestingnessa number of rule having practical relevance o r that can add to the current knowledge in the problem domain we r identified .
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    Application of KDD on Crime Data to Support the Advocacy and Awareness Raising Program of Forum on Street Children Ethiopia
    (Addis Ababa University, 2003-07) Kifle, Woldekidan; Kebede, Gashaw(PhD); Anagaw, Shegaw
    This thesis work gives an account of the process followed to determine the application of KDD to support the advocacy and awareness raising program of FSCE and Addis Ababa Police Commission, and the potential of a data mining learning scheme to discover regularities that underlie the crime dataset. The KDD process as described by Fayyad, Piatetsky-Shapiro and Gregory (1996) that consists of five major phases, namely understanding of the problem domain, data selection, data preprocessing, data mining, and discussion and interpretation was adopted. The discovery task was run on the crime database that consists of 10,878 records/tuples in 17 tables describing a total of 25 attributes. Association rule mining, an exploratory data mining technique was applied to accomplish the goal of the research. To this effect, the Apriori algorithm, which is an implementation of the Association rule in the Weka software, was used. The KDD process can be applied on the crime database to good effect since it can result in rules that can serve as input for the advocacy and awareness raising program. On the basis of subjective (opinions of domain experts) and objective (support and confidence) measures of interestingness, a number of rules having practical relevance or that can add to the current knowledge in the problem domain were identified.