Browsing by Subject "incremental learning"

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Open Access
Learning with feature partitions
(1993) Şirin, İzzet
This thesis presents a new methodology of learning from examples, based on feature partitioning. Classification by Feature Partitioning (CFP) is a particular implementation of this technique, which is an inductive, incremental, and supervised learning method. Learning in CFP is accomplished by storing the objects separately in each feature dimension as disjoint partitions of values. A partition, a basic unit of representation which is initially a point in the feature dimension, is expanded through generalization. The CFP algorithm specializes a partition by subdividing it into two subpartitions. Theoretical (with respect to PAC-model) and empirical evaluation of the CFP is presented and compared with some other similar techniques.
Open Access
Modeling interestingness of streaming association rules as a benefit maximizing classification problem
(2009) Aydın, Tolga
In a typical application of association rule learning from market basket data, a set of transactions for a fixed period of time is used as input to rule learning algorithms. For example, the well-known Apriori algorithm can be applied to learn a set of association rules from such a transaction set. However, learning association rules from a set of transactions is not a one-time only process. For example, a market manager may perform the association rule learning process once every month over the set of transactions collected through the previous month. For this reason, we will consider the problem where transaction sets are input to the system as a stream of packages. The sets of transactions may come in varying sizes and in varying periods. Once a set of transactions arrives, the association rule learning algorithm is run on the last set of transactions, resulting in a new set of association rules. Therefore, the set of association rules learned will accumulate and increase in number over time, making the mining of interesting ones out of this enlarging set of association rules impractical for human experts. We refer to this sequence of rules as “association rule set stream” or “streaming association rules” and the main motivation behind this research is to develop a technique to overcome the interesting rule selection problem. A successful association rule mining system should select and present only the interesting rules to the domain experts. However, definition of interestingness of association rules on a given domain usually differs from one expert to the other and also over time for a given expert. In this thesis, we propose a post-processing method to learn a subjective model for the interestingness concept description of the streaming association rules. The uniqueness of the proposed method is its ability to formulate the interestingness issue of association rules as a benefit-maximizing classification problem and obtain a different interestingness model for each user. In this new classification scheme, the determining features are the selective objective interestingness factors, including the rule’s content itself, related to the interestingness of the association rules; and the target feature is the interestingness label of those rules. The proposed method works incrementally and employs user interactivity at a certain level. It is evaluated on a real supermarket dataset. The results show that the model can successfully select the interesting ones.