Rasli R.M.Rasli R.M.Norwawi N.Md.2024-05-292024-05-292010978077000000010.1109/CIMSiM.2010.802-s2.0-79952087411https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952087411&doi=10.1109%2fCIMSiM.2010.80&partnerID=40&md5=aef4da45b4aded7c03559ea2b72c73e4https://oarep.usim.edu.my/handle/123456789/9809Data mining (DM) is the extraction of hidden predictive information from large databases that has becoming a powerful new technology with great potential to help companies to focus on the most important information in their data warehouses. A predictive model makes a prediction about values of data using known results found from historical data where the best possible outcome based on the previous data is derived. Telekom Malaysia Berhad (TM) is Malaysia's premier communications provider that provides the digital backbone and communication facilities. Direct Exchange Line (DEL) is one of its core telephony services that handle large volume and variety of data in its daily operations. Therefore, it is hard to reveal knowledge structures that can guide decisions in conditions of limited certainty. The main objective of this study is to identify the most appropriate DM techniques (logistic regression, decision trees and neural networks) for predicting DEL growth based on five physical attributes constitute of 672 instances leading to a target (either increase or decrease). The finding is important especially in assisting the prediction of DEL growth in Telekom, thus leading on gaining better understanding on the future of the market based on the current and previous situation. � 2010 IEEE.en-USData miningDELPredictive modeling techniquesTelekom Malaysia BerhadCommunication facilitiesDELHistorical dataKnowledge structuresLarge databaseLimited certaintyLogistic regressionsMalaysiaNew technologiesPredictive informationPredictive modelingPredictive modeling techniquesPredictive modelsTelephony servicesData warehousesDecision treesForecastingNeural networksData miningConference Paper1001055701828