Data mining is having an aim to analyze the observation datasets to find relationship and to present the data in ways that are both understandable and usable. This paper basically focuses on the classification technique of datamining to identify the class of an attribute with an ID3 (classical decision tree approach) and then to add fuzzification to improve the result of ID3.It also contains de...

Bayesian spam filtering is a classification method based on the theory of probability and statistics, and the Bayesian spam filtering based on Mapreduce can solve the defect of the traditional Bayesian spam filtering that consumes large amounts of system resources and network resources when the mail set is pre-training. It needs to classify mails manually in the pre-training phase of mail set, ...

Network traffic have several attributes with different range of values. These attributes can be qualitative or quantitative in nature. Attributes with large values significantly influence the performance of intrusion classifier making it bias towards them. Attribute normalization eliminates such dominance of the attributes by scaling the values of all the attributes within a specific range. The...

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer ...

In this paper, the algorithm was designed of quad-tree spatial search based on spatial information multi-dimensional features, the traditional quad-tree algorithm has been improved to a specific spatial attribute value binding in the quad-tree leaf nodes, a large number with a specific operation irrelevant attributes are excluded, to the effect of finding targets. This algorithm is applied in t...

The 1960s included the earliest discussion on an inductive approach towards the construction of a decision tree for the classification of objects that are described by attributes (Hunt, Marin, & Stone, 1966). It is not until over a decade later that the popularization of the decision tree method was materialized with the introduction of what developed into the Interactive Dichotomizer 3 (Quinla...

While the desire to support fast, ad hoc query processing for large data warehouses has motivated the recent introduction of many new indexing structures, with a few notable exceptions (namely, the LSM-Tree [4] and the Stepped Merge Method [1]) little attention has been given to developing new indexing schemes that allow fast insertions. Since additions to a large warehouse may number in the mi...

The belief decision tree (BDT) approach is a decision tree in an uncertain environment where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function theory. The uncertainty can appear either in the actual class of training objects or attribute values of objects to classify. In this paper, we develop a post-pruning method of belief de...

This paper proposes a novel decision tree for a data set with time-series attributes. Our time-series tree has a value (i.e. a time sequence) of a time-series attribute in its internal node, and splits examples based on dissimilarity between a pair of time sequences. Our method selects, for a split test, a time sequence which exists in data by exhaustive search based on class and shape informat...

Halophile proteins can tolerate high salt concentrations. Understanding halophilicity features is the first step toward engineering halostable crops. To this end, we examined protein features contributing to the halo-toleration of halophilic organisms. We compared more than 850 features for halophilic and non-halophilic proteins with various screening, clustering, decision tree, and generalized...