Decision Tree Overview
In data mining techniques two kinds of basic learning processes are available namely supervised and unsupervised. when we talk about the supervised learning techniques the decision tree learning is one of the most essential technique of classification and prediction. A number of different kinds of decision tree algorithms are available i.e. ID3, C4.5, C5.0, CART, SLIQ and others. All these algorithms the used to generate the transparent data models. these data models can be evaluated using the paper and pencil. therefore that is an effective data modeling technique.
Applications of Decision Trees
- Application of Decision Tree Algorithm in Healthcare Operations : the decision trees are used to visualize the data patterns in form of tree data structure. that help to also prepare the relationship among the attributes and the final class labels. thus the patient’s different health attributes can help to understand the symptoms and possibility by comparing the historical data available with the similar attributes.
- Manufacturing and Production: in a large production based industries where the regulation of production and planning is required. the decision tree models helps for understanding the amount of production, time of production and other scenarios. that can be evaluated using the past scenarios of the company productions and manufacturing.
- Agriculture: Dis-aggregation of conventional soil surveys has been identified as a potential source for much of the next generation of model-ready digital soil spatial data. This process aims to apportion vector soil surveys into raster representations of the component soils that are often aggregated together in map unit designs. Most soil surveys are published with some description of the soil–landscape relationships that distinguish component soils within map units. The classification tree ensembles technique is used to a grid soil series map .
- Financial analysis: Decision trees are also applicable to marketing and business development operations. The general setting for these types of cases is similar to that of real option pricing. Basically, companies are constantly making decisions regarding product expansion, marketing operations, international expansion, international contraction, hiring employees or even merging with another company. Organizing all considered alternatives with a decision tree allows for a systematic means to evaluate these ideas simultaneously .
- credit card fraud detection: according to  the decision trees also help to understand the normal patterns of credit card transactions and the fraud transactions.
Example of decision making
figure 1 decision tree
the above given figure 1 demonstrate the example of decision tree. in this example the play tennis example is described. let the current weather condition “sunny” and “humidity” is “normal”. the according to the given scenario the tree is invoked according to available attribute values and the final decision is found the player can play tennis “Yes”.
Farhad Soleimanian Gharehchopogh, Peyman Mohammadi, Parvin Hakimi, “Application of Decision Tree Algorithm for Data Mining in Healthcare Operations: A Case Study”, International Journal of Computer Applications (0975 – 8887) Volume 52 – No. 6, August 2012
Travis W. Nauman, James A. Thompson, “Semi-automated disaggregation of conventional soil maps using
knowledge driven data mining and classification trees”, Geoderma 213 (2014) 385–399
 Snehal Patil, Harshada Somavanshi, Jyoti Gaikwad, Amruta Deshmane, Rinku Badgujar, “Credit Card Fraud Detection Using Decision Tree Induction Algorithm”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.4, April- 2015, pg. 92-95