We study the quantum version of a decision tree classifier to fill the gap between quantum computation and machine learning. The quantum entropy impurity criterion which is used to determine which node should be split is presented in the paper. By using the quantum fidelity measure between two quantum states, we cluster the training data into subclasses so that the quantum decision tree can man...

Data mining can be performed with four different directions Association, Clustering, Classification and the Bayesian formula. In the recent years, many privacy preserving techniques are developed. These techniques are used to provide the privacy to the data. Here privacy related various information is gathered with their brief introduction. Here mainly focus is on the classification decision tr...

Decision tree construction is an important data mining problem. In this paper, we revisit this problem, with a new goal, i.e. Can we develop an efficient parallel algorithm for decision tree construction that can be parallelized in the same way as algorithms for other major mining tasks ?. We report a new approach to decision tree construction, which we refer to as SPIES (Statistical Pruning of...

The Very Fast Decision Tree (VFDT) is one of the most important classification algorithms for real-time data stream mining. However, imperfections in data streams, such as noise and imbalanced class distribution, do exist in real world applications and they jeopardize the performance of VFDT. Traditional sampling techniques and post-pruning may be impractical for a non-stopping data stream. To ...

We study impurity-based decision tree algorithms such as CART, C4.5, etc., so as to better understand their theoretical underpinnings. We consider such algorithms on special forms of functions and distributions. We deal with the uniform distribution and functions that can be described as a boolean linear threshold functions or a read-once DNF. We show that for boolean linear threshold functions...

Decision tree induction has been has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe, or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms...

Results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. In this study, damage spectrum and C4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. In order to prepare the damage spectrum, steel buildings were modeled as a single-degree-of-freedom (SDOF) system and time-history...

We introduce Propositionalized Attribute Taxonomy guided Decision Tree Learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce Propositionalized Attribute Taxonomy Learner (PAT-Learner) that automatically constructs taxono...

finding frequent patterns plays a key role in exploring association patterns, correlation, and many other interesting relationships that are applicable in tdb. several association rule mining algorithms such as apriori, fp-growth, and eclat have been proposed in the literature. fp-growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract...

We report on work towards exible algorithms for solving decision problems represented as innuence diagrams. An algorithm is given to construct a tree structure for each decision node in an innuence diagram. Each tree represents a decision function and is constructed incrementally. The decision maker can balance the cost of computing the next incremental improvement to a tree against the expecte...