Real-time monitoring of the data of the thermal power plant is the basis of accurate analyzing thermal economy and accurate reconstruction of the operating state. Due to noise is inevitable, we need real-time monitoring data filtering to get accurate information of units and equipment in the operating data of thermal power plant. Real-time filtering algorithm can’t be used to correct the curren...

This paper discusses an overview of data mining techniques for personalization. It discusses some of the standard techniques which are used in order to adapt and increase the ability of the system to tailor itself to specific user behavior. We discuss several such techniques such as collaborative filtering, content based methods, and content based collaborative filtering methods. We examine the...

We consider a direct mail problem in which a system repeats the following process everyday during some period: select a set of user-item pairs (u, i), send a recommendation mail of item i to user u for each selected pair (u, i), and receive a response from each user. We assume that each response can be obtained before the next process and through the response, the system can know the user’s eva...

The Heart sounds and murmurs provide crucial diagnosis information for several heart diseases such as natural or prosthetic valve dysfunction and heart failure. Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advanc...

We propose a probabilistic matrix factorization model for collaborative filtering that learns from data that is missing not at random (MNAR). Matrix factorization models exhibit state-of-the-art predictive performance in collaborative filtering. However, these models usually assume that the data is missing at random (MAR), and this is rarely the case. For example, the data is not MAR if users r...

Biomedical datasets usually include a large number of features relative to the number of samples. However, some data dimensions may be less relevant or even irrelevant to the output class. Selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. To this end, this paper presents a hybrid method of filter and wr...