Decision tree is widely used in machine learning. In the process of constructing a tree, appropriate attributes have to be selected as nodes of the tree based on some criteria. There are several approaches to selection of attributes. In this paper, we present a new approach to selection of attributes for construction of decision tree based on rough set theory. The basic idea is, if the size of ...

Decision trees have been found very effective for classification especially in Data Mining. This paper aims at improving the performance of the SLIQ decision tree algorithm (Mehta et. al,1996) for classification in data mining The drawback of this algorithm is that large number of gini indices have to be computed at each node of the decision tree. In order to decide which attribute is to be spl...

This paper will propose a novel star schema attribute induction as a new attribute induction paradigm and as improving from current attribute oriented induction. A novel star schema attribute induction will be examined with current attribute oriented induction based on characteristic rule and using non rule based concept hierarchy by implementing both of approaches. In novel star schema attribu...

Attributed tree transducers are formal models for studying properties of attribute grammars. In this paper we prove a two{dimen-sional hierarchy for classes of producing attributed tree transducers. The two dimensions refer to the maximum number of synthesized and inherited attributes, respectively. To this end we apply a pumping lemma for output languages of producing attributed tree transducers.

Increasing growth of functionality in current IT trends proved the decision making operations through mass data mining techniques. There is still a requirement for further efficiency and optimization. The problem of constructing the optimization decision tree is now an active research area. Generating an efficient and optimized decision tree with multi-attribute data source is considered as one...

This paper compares two artificial intelligence methods the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the attribute (feature) transformations, which allows users to input formulas and create new attributes. Al...

This paper describes an attribute based tabu search heuristic for the Generalized Minimum Spanning Tree Problem (GMSTP) known to be NP-hard. Given a graph whose vertex set is partitioned into clusters, the GMSTP consists of designing a minimum cost tree spanning all clusters. An attribute based tabu search heuristic employing new neighborhoods is proposed. An extended set of TSPLIB test instanc...

We present here the research work on data mining technologies for complicated attributes relationship in digital library collections. Firstly our work and ideology is introduced as the research background of this paper. Digital library evaluation is an important topic in information systems domain. We creatively import data mining technologies into it to get an intelligent decision support. But...

Considering the theory of attribute grammars, we use logical formulas instead of traditional functional semantic rules. Following the decoration of a derivation tree, a suitable algorithm should maintain the consistency of the formulas together with the evaluation of the attributes. This may be a Prolog-like resolution, but this paper examines a somewhat different strategy, based on production ...

In this paper, we discuss the power conservative indexing techniques for managing multi-attribute data broadcast on wireless channels. These indexing techniques, namely, index tree, signature and hybrid, aim at improving the battery power consumption of mobile clients. By taking into account the broadcast management factors such as clustering and scheduling, these three indexing schemes may sig...