Big Data with Decision Tree Induction Big data mining is one of the major challenging research issues in the field of machine learning for data mining applications in this present digital era. Big data consists of 3Vxe2x80x99s: (1) volume  massive amount of datatoo many bytes, (2) velocity  high speed streaming datatoo high a rate, and (3) variety  data are coming from different sourcestoo many sources. Collecting and managing reallife big data is a difficult task, as big data is so big that we cannot keep all the data together in a single machine. Therefore, we need advanced relational database management systems with parallel computing to deal with big data. Knowledge mining from big data employing traditional machine learning and data mining techniques is a big issue and attract computational intelligent researcher in this area. In this paper, we have used the decision tree (DT) induction method for mining big data. Decision tree induction is one of the most preferable and wellknown supervised learning technique, which is a topdown recursive divide and conquer algorithm and require little prior knowledge for constructing a classifier. The traditional DT algorithms like Iterative Dichotomiser 3 (ID3), C4.5 (a successor of ID3 algorithm), Classification and Regression Trees (CART) are generally built for mining relatively small datasets. So, we need a more scalable decision tree learning approach for mining big data. In this paper, we have engendered several trees employing two scalable decision tree algorithms: RainForest Tree and Bootstrapped Optimistic Algorithm for Tree construction (BOAT) using seven benchmark datasets from Keel Repository and UCI Machine Learning repository. We have compared the performance of RainForest and BOAT algorithms. Also, we have proposed a decision tree merging approach, as decision tree merging is a very complex and challenging task.

An Online TreeBased Approach for Mining NonStationary HighSpeed Data Streams This paper presents a new learning algorithm for inducing decision trees from data streams. In these domains, large amounts of data are constantly arriving over time, possibly at high speed. The proposed algorithm uses a topdown induction method for building trees, splitting leaf nodes recursively, until none of them can be expanded. The new algorithm combines two split methods in the tree induction. The first method is able to guarantee, with statistical significance, that each split chosen would be the same as that chosen using infinite examples. By doing so, it aims at ensuring that the tree induced online is close to the optimal model. However, this split method often needs too many examples to make a decision about the best split, which delays the accuracy improvement of the online predictive learning model. Therefore, the second method is used to split nodes more quickly, speeding up the tree growth. The second split method is based on the observation that larger trees are able to store more information about the training examples and to represent more complex concepts. The first split method is also used to correct splits previously suggested by the second one, when it has sufficient evidence. Finally, an additional procedure rebuilds the tree model according to the suggestions made with an adequate level of statistical significance. The proposed algorithm is empirically compared with several wellknown induction algorithms for learning decision trees from data streams. In the tests it is possible to observe that the proposed algorithm is more competitive in terms of accuracy and model size using various synthetic and real world datasets.