Browsing by Author "Getachew, Mesfin (PhD)"

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    The Application of Data Mining to Support Customer Relationship . Management at Ethiopian Airlines
    (Addis Ababa University, 2003-06) Abera, Denekew; Getachew, Mesfin (PhD)
    Airlines are being pushed to understand and quickly respond to the individual needs and wants of their customers due to the dynamic and highly competitive nature of the industry. Most airlines use frequent flyer incentive programs and maintain a database of their frequent flyer customers to win the loyalty of their customers, by awarding points that entitle customers to various travel benefits. Customer relationship management (CRM) is the overall process of exploiting customer- related data and information, and using it to enhance the revenue flow from an existing customer. As part of implementing CRM, airlines use their frequent flyer databases to get a better understanding of their customer types and behavior. Data mining techniques playable here by allowing to extract important customer information from available databases. This study is aimed at assessing the application of data mining techniques to support CRM activities at Ethiopian Airlines. The subject of this case study, the Ethiopian Airlines' frequent flyer program, has a database that contained individual flight activity and demographic information of over 35,000 program members Having the objective of filling the gap left by a related research, which was carried out by Henok (2002), this study has used the data mining database prepared by Henok (2002). In the course of using the database to attain the objective of this research, a data preparation tasks such as driving new attributes from the existing original attributes, defining new attributes and then preparing new data tables were done.The data mining process in this research is divided into two major phases. During the first phase, since there has been an attempt to use three different data mining software, data was prepared and formatted into the appropriate format for the respective data mining software to be used. The second phase, which is model building phase, was addressed in two sub-phases, the clustering sub-phase and the classification sub-phase, the major contribution of this study. In the clustering sub phase the K-means clustering algorithm was used to segment individual customer records into clusters with similar behaviors. In the classification sub-phase, J4.B and J4.B PART algorithms were employed to generate rules that were used to develop the predestined model that assigns new customer records into the corresponding segments As a final output of this research, a prototype of Customer Classification System is developed. The prototype enables to classify a new customer into one of the customer clusters, generate cluster results, search for a customer and find the cluster where the customer belongs, and also provides with the description of each customer clusters The results from this study were encouraging and confirmed the belief that applying data mining techniques could indeed support CRM activities at Ethiopian Airlines. In the future, more segmentation and classification studies by using a possible large amount of customer records with demographic information and employing other clustering and classification algorithms could yield better results
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    An Automatic Sentence Parser for Oromo Language Using Supervised Learning Technique
    (Addis Ababa University, 2002-06) Megersa, Diriba; Getachew, Mesfin (PhD); Meshesha, Million (PhD)
    The goal of Informal ion Retrieval has been to reduce human language complexities and as a result serve users in The mos I efficient way. The decisive in achieving such end is the Natural language Processing (NLP). NLP has many components in serving such purpose. Parsing is one of such components in NLP in improving precision and calligraphic is The goal of Informal ion Retrieval Systems. Moreover, parsing is also used inhere{for warlords machine Translation which is one of the hear of Natural Language Processing. Today, difference kinds of parsers have been developed' languages. lhis hare relatively wider use nationally and/or international/ly since The 1960.1. Un[unalterably Gromo has nol captured Ihe advanlage of such .Iyslem being Ihe working language of Ihe Slale Government of Gromiya, and one of Ihe major languages in Elhiopia and Ababa (Abebe 2002) lor Ihere are no syslems (parsers of any sarI) Ihal parse wril/en lexlS in Ihis language. This siudy is, Iherefore, an allempl 10 develop a simple aulomalic .lenIence parser for Oromo language In Ihe sludy, Ihe chari algorilhm 11 '0.1 used lI'ilh some modi/iealion. A module (or mOlphological analyzer, which splils words inlo roOI form and Iheir wrresponding morpheme, was also developed in order 10 faeil ilale Ihe preparalion of lexls in a lile 10 be parsed wilh appropriale lexical calegories. In addition, The unsupervised learning algorilhm was designed 10 guide The parser in predicting unknown and ambiguous words in a sentence. Grammar rules, lexicon, morphological rules and lexicon in-formalin were also designed on The basis of Ihe review Decide on Ihe linguistic propellers of amII/o grumll1alical categories. This system, facing, is the firslinils kind fiJI' this language. The study adopts an intelligent (Rule-Based+ learning Inodule) approach to develop a prototype. which is a simple Drama parser/or the language. The thesis. in short. describes processes a/automated sentence parsing oj' Free Texts. That is, it is aimed at developing a prototype and conducting an experimel with it. The result obtained (95% on the training test and 885% on the test set) using the small manually parsed sentences encourage birther research to be launched. especially with the aim of developing fill~fledged Oromo sentence parser.
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    Development of Stemming Algorithm for Wolaytta Text
    (Addis Ababa University, 2003-07) Lessa, Lemma; Getachew, Mesfin (PhD); Alemu, Atelach (PhD); Eyesus, Haile (PhD)
    This study describes the design of a stemming algorithm for Wolaytta language. To give a solid background for the thesis, literature on conflation in general and stemming algorithms in particular were reviewed. Since it is the nature and characteristics of suffixation that guide the development of steamer, the Wolaytta language morphology was studied and described in order to model the language and develop an automatic procedure for conflation. The inflectional and derivational morphologies of the language are discussed. It is indicated that suffixation is the main word formation process in Wordplay language. It is also attempted to show that the language is morphological complex and uses extensive concatenation of suffixes The result of the study is a prototype context sensitive iterative stemmer for Wolaytta language. Error counting technique was employed to evaluate the performance of this stemmer. The stemmer was trained on 3537 words (80% of the sample text) and the improved version reveals an accuracy of 90.6% on the training set. The number of over stemmed and understeml11ed words on the training set were 8.6% (304 words) and 0.8% (28 words) respectively. When the stemmer rW1S on the unseen sample of 884 words (20% of the sample text), it performed with an accuracy of 86.9%. The percentage of endorser recorded as under stunned and over stemmed on this unseen (test set) were 9% and 4.1 %, respectively. Moreover, a dictionary reduction of 38 .92% was attained on the test set. The major sources of errors are also reported with possible recommendations to further improve the performance of the stemmer and also for further research.
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    The Role of Data Mining in the Risk Assessment of Customs: (With special reference to The Ethiopian Customs Authority)
    (Addis Ababa University, 2004-07) Belew, Girma; Desai, B.L. (PhD); Getachew, Mesfin (PhD)
    Customs Organizations are responsible for two Opposing In g yet equally important responsibilities . These are t e provision of efficient services to traders for the smooth flow of shipments and the protection of the country from any kind of risk threats that is associated with interactional trade. Reform and monetization of custom services through automation and setting transparent working procedures has resulted in the provision of efficient services . However addressing the issue of implementing a proper risk management strategy remain s a challenge The Ethiopian Customs Authority at present is handling the control of customs risks using subjective methods. The subjective method of handling risks solely depends on experts' judgment of selecting shipments for physical examination. The Subjective method is essential since the knowledge and experience of customs experts and their observation of the behaviors of intervening agents like traders and clearing agents is very important. However depending only on subjective analysis for strategic risk management ha s its shortcomings. This study was aimed at supporting the current selective physical examination system o f incoming shipments in the Ethiopian Customs Authority with objective methods using data mining. The study was conducted through the annals is of customs fraud cases seized in the past. For this stud y one of the data mining techniques known as decision tree was employed. The dataset used in the stud y consisted of 10 364 record s out of which 17 0 cases were fraud cases. The distribution of the two classes was highly imbalanced. To deal with the class imbalance pro blend the over- sampling approach was use d. Five experiments we re conducted by varying the rate of over-sampling. After over- sampling the five datasets had a ratio of 90: 10, 80:20, 70:30, 60:40 and 50 :50 all non- fraud to fraud. Using an independent dataset that also contain 2616 non-fraud and 39 fraud cases all the models generated by the five data sets were validated. The dataset with a proportion of 70:30 has shown the best result in terms of correctly classifying the fraud cases. The model correctly classified 22 fraud cases out of the 39 cases. Combining the subjective method with the subjective methods can improve the efficiency of risk assessment and selective physical examination of shipments in the Ethiopian customs authority. Models developed by automatic analysis can also be used across all the different customs offices in the country consistently.