Now showing 1 - 2 of 2
  • Publication
    Distributed Clustering Algorithm for Spatial Data Mining
    Distributed data mining techniques and mainly distributed clustering are widely used in last decade because they deal with very large and heterogeneous datasets which cannot be gathered centrally. Current distributed clustering approaches are normally generating global models by aggregating local results that are obtained on each site. While this approach analyses the datasets on their locations the aggregation phase is complex, time consuming and may produce incorrect and ambiguous global clusters and therefore incorrect knowledge. In this paper we propose a new clustering approach for very large spatial datasets that are heterogeneous and distributed. The approach is based on K-means Algorithm but it generates the number of global clusters dynamically. It is not necessary to fix the number of clusters. Moreover, this approach uses a very sophisticated aggregation phase. The aggregation phase is designed in such away that the final clusters are compact and accurate while the overall process is efficient in time and memory allocation. Preliminary results show that the proposed approach scales up well in terms of running time, and result quality, we also compared it to two other clustering algorithms BIRCH and CURE and we show clearly this approach is much more efficient than the two algorithms.
  • Publication
    A Fuzzy Rule-based Learning Algorithm for Customer Churn Prediction
    Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However most of rulebased learning algorithms designed with the assumption of well-balanced datasets, may provide unacceptable prediction results. This paper introduces a Fuzzy Association Rule-based Classification Learning Algorithm for customer churn prediction. The proposed algorithm adapts CAIM discretization algorithm to obtain fuzzy partitions, then searches a set of rules using an assessment method. The experiments were carried out to validate the proposed approach using the customer services dataset of Telecom. The experimental results show that the proposed approach can achieve acceptable prediction accuracy and efficient for churn prediction.
    Scopus© Citations 2  641