Data mining deals with various applications such as the discovery of hidden knowledge, unexpected patterns and new rules from large Databases that guide to make decisions about enterprise to products and services competitive. Basically, data mining is concerned with the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. Data Mining, which ...

Risk bounds for Classification and Regression Trees (CART, Breiman et. al. 1984) classifiers are obtained under a margin condition in the binary supervised classification framework. These risk bounds are obtained conditionally on the construction of the maximal deep binary tree and permit to prove that the linear penalty used in the CART pruning algorithm is valid under a margin condition. It i...

A hybrid intelligent classiier is built for pattern classiication. It consists of a classiication and regression tree (CART), a genetic algorithm (GA) and a neural network (NN). CART extracts features of the patterns by setting up decision rules. Rule improvement by GA is explored. The rules act as a pre-processing layer of NN, a multi-class neural classiier, through which the most probable cla...

The goal of frequent pattern mining is to determine the frequently occurring group of items in the databases. Here the major contributing task is expediting the frequent itemset by proposing a technique that uses the minimal data available in the shopping cart for the prediction of what other items the customer can get the choice to buy. Several algorithms have been implemented to detect the fr...

Description of prediction guide The women’s most recent minimum data set (MDS) (collected on all nursing home residents in the US) was the primary source of information for the algorithm (total 75 variables). The data pertained to physical functioning and activities of daily living, ambulatory status, vision status, mood and behaviour patterns, incontinence, dementia, other health conditions, a...

Human population growth and land use cover (LULC) change have always developed side by side. Considering selection of a good Machine Learning (ML) classifier algorithm is needed considering the high estimation LULC maps based on remote sensing. This study aims to produce classification Landsat-8 Sentinel-2 images comparing accuracy performance three ML algorithms, namely: Classification Regress...

Many researches has focused mainly on how to expedite the search for frequently co-occurring groups of items in “shopping cart” and less attention has been paid to the methods that exploit these “frequent itemsets” for prediction purposes. This study contributes to this task by proposing a technique that uses the partial information about the contents of a shopping cart for the prediction of wh...

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem “cart-pole”. In this paper we have tried to train the Artific...

In this paper, we study the use of boosted weak classifiers selected with AdaBoost algorithm in object detection. Our work is motivated by the good performance of AdaBoost in selecting discriminative features and the effectiveness of Classification and Regression Tree (CART) compared with other classification methods. First, we study the cascaded structure of the boosted weak classifier detecto...