Decision tree is one of the most significant classification methods applied in data mining. By its graphic output, users could have an easy way to interpret the decision flow and the mining outcome. However, decision tree is known to be time consuming. It will spend a high computation cost ...
In data mining there are four main problems, clustering, classifying, regression and dimension reduce, to be discussed. And this issue is mainly about Decision Tree in classification. For some data that we’ve known, calculate the decision tree, and use the tree to deal with new points, tell...
Since the data is collected from disparate sources in many actual data mining environments, it is common to have data values in different abstraction levels. This paper shows that such multiple abstraction levels of data can cause undesirable effects in decision tree classification. After explaining ...
Decision tree is a widely used classification in data mining. It can discover the essential knowledge from the common decision tables (each row has a decision). However, it is difficult to do data mining from the multi-label decision tables (each row has a set of decisions). In a multi-...
Decision tree (decision tree), also known as decision tree is a kind of information theory-based, decision tree data structure based on this classification algorithm. Categorized the decision tree in the field of data mining has been studied for many years, and had a lot of algorithms, such ...
Comparative Study of K-NN , Naive Bayes and Decision Tree Classification Techniques Classification is a data mining technique used to predict group membership for data instances within a given dataset. It is used for classifying data into different classes by considering some constrains. The problem ...
Data Mining - Decision Tree (DT) Algorithm Desicion Tree (DT) are supervised Classification algorithms. They are: easy to interpret (due to the tree structure) a boolean function (If each decision is binary ie false or true) Decision trees... Machine Learning - (Overfitting|Overtraining|Robus...
Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by...
We also compare performance with a very successful and well established decision tree classifier. Results:T3 demonstrated impressive performance when predicting unseen cases of stroke resulting in as little as 0.4% classification error while the state of the art decision tree classifier resulted in 33.6...
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning Classification is an important problem in data mining. Given a database of records, each with a class label, a classifiergenerates a concise and meaningful... RK Shim - 《Data Mining & Knowledge Discovery》 被引量: 563发表...