M Tech Dissertations
Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3
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Item Open Access Collaborative filtering approach with decision tree technique(Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Srivastava, Anit; Jotwani, Naresh D.Rapid advances in data collection and storage technology has enabled organizations (especially e-commerce) to accumulate vast amounts of data. The amount of data kept in computer files and databases is growing at a phenomenal rate because customers are evolving to use e- commerce services. So processing of large number of coustomer’s past purchase records is becoming a new challenge in e-commerce. The primary goal of e-commerce services is to build the systems where customers can get their likely recommended products relevant to their past purchase. We have implemented collaboratives filtering with supervised learning techniques. One of supervised learning techniques is Decision Tree. We have used Decision Tree to cluster similar type of customers according to active customer preferences (or tastes). In our new approach, a collaborative filtering based recommender system will recommended Top-k likely products according to customers preferences (or tastes) by considering past purchase record (or implicit ratings) of its clustered customers. This system will also recommend or predict Top-k likely products to particular customers by considering the cases when clustered customers have given explicit ratings (or votes) to their previously purchased products.Item Open Access Application of BTrees in data mining(Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Srivastava, Amit; Jotwani, Naresh D.As massive amount of information are becoming available electronically, techniques for making the decision to analyze statistics on the large dataset are tending to be very complex. Making of such a decision requires more disk accesses in the main memory. So there is a need of such important techniques which can take least number of disk accesses as well as less running time to perform some operations in the main memory. Building of such a strategic goal oriented decision, there is requisite to classify the information into different classes with the help of some given properties of the information which enabled us to make two BTrees that are running simultaneous. One BTree is used as a classifier for making the decision and another bTree maintains the organization of the information of dataset from where we make the strategic decisions. Our research embodies around the learning, implementation and usage of advances data structure (i.e. BTree). In our thesis work we have used the binary search approach instead of the linear search takes running time O (T), has enhanced the performance of the BTree during execution of the operations on the BTree.