Add to ORKGThis paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case
G-means is a data mining clustering algorithm based on k-means, used to find the number of Gaussian ...
The set of algorithms and techniques used to extract interesting patterns and trends from huge data ...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...
This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms...
Abstract This paper investigates scalable implementations of out-of-core I/O-intensiv e Data Mining ...
In this paper, we studied the parallelization of K-Means clustering algorithm, proposed a parallel s...
Handling and processing of larger volume of data requires efficient data mining algorithms. k-means ...
K-means algorithm is one of the most widely used methods in data mining and statistical data analysi...
The amount of information that must be processed daily by computer systems has reached huge quantiti...
Global communication requirements and load imbalance of some parallel data mining algorithms are the...
Abstract Recent years have shown the need of an automated process to discover interesting and hidden...
Abstract. To cluster increasingly massive data sets that are common today in data and text mining, w...
At present, the explosive growth of data and the mass storage state have brought many problems such ...
Current microarchitectures are equipped with SIMD instruction sets enabling massive data parallelism...
The K-Means algorithm for cluster analysis is one of the most influential and popular data mining me...
G-means is a data mining clustering algorithm based on k-means, used to find the number of Gaussian ...
The set of algorithms and techniques used to extract interesting patterns and trends from huge data ...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...
This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms...
Abstract This paper investigates scalable implementations of out-of-core I/O-intensiv e Data Mining ...
In this paper, we studied the parallelization of K-Means clustering algorithm, proposed a parallel s...
Handling and processing of larger volume of data requires efficient data mining algorithms. k-means ...
K-means algorithm is one of the most widely used methods in data mining and statistical data analysi...
The amount of information that must be processed daily by computer systems has reached huge quantiti...
Global communication requirements and load imbalance of some parallel data mining algorithms are the...
Abstract Recent years have shown the need of an automated process to discover interesting and hidden...
Abstract. To cluster increasingly massive data sets that are common today in data and text mining, w...
At present, the explosive growth of data and the mass storage state have brought many problems such ...
Current microarchitectures are equipped with SIMD instruction sets enabling massive data parallelism...
The K-Means algorithm for cluster analysis is one of the most influential and popular data mining me...
G-means is a data mining clustering algorithm based on k-means, used to find the number of Gaussian ...
The set of algorithms and techniques used to extract interesting patterns and trends from huge data ...
Advances in hardware and software technology enable us to collect, store and distribute large quanti...