Data Mining and Knowledge Engineering

D. Muruga Radha Devi ¹, P. Thambidurai ²

Affiliations
1 Sathyabama University, Chennai, Tamil Nadu, IN
2 Computer Science & Engineering Department, Perunthalaivar Kamarajar College of Institute and Technology, Karaikal, Pondicherry, IN

Abstract

Similarity search in time series databases is an important research direction. Research in this field has focused on the development of effective transformation techniques, the application of dimensionality reduction methods and the design of efficient indexing schemes. In the case where time series are continuously updated with new values (streaming time series), the similarity problem becomes even more difficult to solve, since we must take into consideration the new values of the series. The challenge in a streaming database is to provide efficient algorithms and access methods for query processing, taking into consideration the fact that the database changes continuously as new data become available. Traditional access methods that continuously update the data are considered inappropriate, due to significant update costs. To attack the problem, significant research has been performed towards the development of effective and efficient methods for streaming time series processing. In this paper, we introduce the most important issues concerning similarity search in recent biased time series databases, presenting an efficient technique for similarity query processing in streaming time series using sliding windows. The proposed method called as adaptive stream processing is based on an incremental computation of Discrete wavelet transform which is used as a feature extraction method. In order to prove the efficiency of the proposed method, experiments have been performed for range query and k-nearest neighbor query on real-life data sets. The results have shown that the adaptive stream processing method exhibit consistently better performance in comparison to previously proposed approaches.

Keywords

Similarity Search, Recent Biased Time Series, Sliding Window, Feature Extraction, Stream Processing.

Full Text

     

A. Kumar ¹, P. Thambidurai ²

Affiliations
1 Department of Computer Science & Engineering, Sathyabama University, Chennai, IN
2 Department of Computer Science & Engineering, Pondicherry Engineering College, Puducherry, IN

Abstract

Web-based product and recommendation systems have become ever popular on-line business practice with increasing emphasis on modeling customer needs and providing them with targeted or personalized service solutions in real-time interaction. Recommender systems is a specific type of information filtering system technique that attempts to recommend information items like images, web pages, etc that are likely to be of interest to the user. Normally, a recommender system compares a user profile to some reference characteristics, and seeks to predict the 'rating' and retrieve the query elements. This system can be classified into two groups: one is Content-based recommendation and another is collaborative recommendation system. Content based recommendation tries to recommend web sites similar to those web sites the user has liked, whereas collaborative recommendation tries to find some users who share similar tastes with the given user and recommends web sites they like to that user. Based on web usage data in adoptive association rule based web mining the association rules were applied to personalization. The technique makes use of apriori algorithm to generate association rules. Even this method has some disadvantages. An effective Fuzzy Association Rule Mining Algorithm (FARM) is proposed by the author to overcome those disadvantages. This proposed Fuzzy HARM algorithm for association rule mining in web recommendation system results in better quality and performance.

Keywords

Fuzzy Healthy Association Rule Mining, Association Rules, Apriori Algorithm, Collaborative Recommender.

Full Text