Add to ORKGLearning decision trees against very large amounts of data is not practical on single node computers due to the huge amount of calculations required by this process. Apache Hadoop is a large scale distributed computing platform that runs on commodity hardware clusters and can be used successfully for data mining task against very large datasets. This work presents a parallel decision tree learning algorithm expressed in MapReduce programming model that runs on Apache Hadoop platform and has a very good scalability with dataset size
Classification is an important data mining problem. Although classification is a wellstudied problem...
When running data-mining algorithms on big data platforms, a parallel, distributed framework, such a...
One of the important problems in data mining is classification. Recently there has been a lot of int...
In this paper, we present ScalParC (Scalable Parallel Classifier), a new parallel formulation of a d...
In this paper, we present ScalParC (Scalable Parallel Classifier), a new parallel formulation of a d...
With the emergence of big data, inducting regression trees on very large data sets became a common d...
Classification of very large datasets is a challenging problem in data mining. It is desirable to h...
Abstract Data-intensive computing has received substantial attention since the arrival of the big da...
Machine Learning (ML) is at the core of data analysis. Machine Learning Algorithms (MLA) are sequent...
Today, we are living in a data-exploding era, in which the volume of data is expanding in an unbelie...
Abstract. In the fields of data mining and machine learning the amount of data available for buildin...
Abstract—Decision tree construction is a well-studied data mining problem. In this paper, we focus o...
Data mining refers to the process of finding hidden patterns inside a large dataset. While improving...
Abstract Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Fores...
Univariate decision tree algorithms are widely used in Data Mining because (i) they are easy to lear...
Classification is an important data mining problem. Although classification is a wellstudied problem...
When running data-mining algorithms on big data platforms, a parallel, distributed framework, such a...
One of the important problems in data mining is classification. Recently there has been a lot of int...
In this paper, we present ScalParC (Scalable Parallel Classifier), a new parallel formulation of a d...
In this paper, we present ScalParC (Scalable Parallel Classifier), a new parallel formulation of a d...
With the emergence of big data, inducting regression trees on very large data sets became a common d...
Classification of very large datasets is a challenging problem in data mining. It is desirable to h...
Abstract Data-intensive computing has received substantial attention since the arrival of the big da...
Machine Learning (ML) is at the core of data analysis. Machine Learning Algorithms (MLA) are sequent...
Today, we are living in a data-exploding era, in which the volume of data is expanding in an unbelie...
Abstract. In the fields of data mining and machine learning the amount of data available for buildin...
Abstract—Decision tree construction is a well-studied data mining problem. In this paper, we focus o...
Data mining refers to the process of finding hidden patterns inside a large dataset. While improving...
Abstract Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Fores...
Univariate decision tree algorithms are widely used in Data Mining because (i) they are easy to lear...
Classification is an important data mining problem. Although classification is a wellstudied problem...
When running data-mining algorithms on big data platforms, a parallel, distributed framework, such a...
One of the important problems in data mining is classification. Recently there has been a lot of int...