DD-Rtree: A dynamic distributed data structure for efficient data distribution among cluster nodes for spatial data mining algorithms

2016 ◽  
Author(s):  
Jagat Sesh Challa ◽  
Poonam Goyal ◽  
S. Nikhil ◽  
Aditya Mangla ◽  
Sundar S. Balasubramaniam ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Structure ◽  
Spatial Data ◽  
Data Distribution ◽  
Spatial Data Mining ◽  
Distributed Data ◽  
Data Mining Algorithms ◽  
Efficient Data ◽  
Mining Algorithms

OntoSDM: An Approach to Improve Quality on Spatial Data Mining Algorithms

2015 ◽  
pp. 555-565
Author(s):  
Carlos Roberto Valêncio ◽  
Diogo Lemos Guimarães ◽  
Geraldo F. D. Zafalon ◽  
Leandro A. Neves ◽  
Angelo C. Colombini
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Improve Quality ◽  
Data Mining Algorithms ◽  
Mining Algorithms

scholarly journals Implementation of Data Mining Grouping Product Order Distribution Center In PT. Indomarco Prismatama Medan Branch

2020 ◽  
Vol 2 (1) ◽  
pp. 100-106
Author(s):  
Nico Setiawan ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Data Distribution ◽  
Distribution Center ◽  
Data Mining Algorithms ◽  
Data Grouping ◽  
Mining Algorithms ◽  
Product Order

Data mining is the technique of extracting previously unknown information in a set of data in the database. Data mining has been applied in various fields which require extracting information. One of them in groupings of data. Grouping is used to divide a set of data into several sections that are useful to more easily identify a class of data. Distribution companies can use groupings of one to determine the intensity of the volume of goods ordered. The study analyzes the application of data mining algorithms k-meansclustering to elicit information from the data ordering goods contained in centerPT distribution. Indomarco Prismatama Medan branch. That is by using a number of items and the total amount of the quantity of each item ordered.

scholarly journals Cloud for Distributed Data Analysis Based on the Actor Model

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ivan Kholod ◽  
Ilya Petukhov ◽  
Andrey Shorov
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Data Sets ◽  
Distributed Data ◽  
Actor Model ◽  
Confidential Information ◽  
Data Mining Algorithms ◽  
Functional Blocks ◽  
Mining Algorithms ◽  
Distributed Data Analysis

This paper describes the construction of a Cloud for Distributed Data Analysis (CDDA) based on the actor model. The design uses an approach to map the data mining algorithms on decomposed functional blocks, which are assigned to actors. Using actors allows users to move the computation closely towards the stored data. The process does not require loading data sets into the cloud and allows users to analyze confidential information locally. The results of experiments show that the efficiency of the proposed approach outperforms established solutions.

The Research of High Efficient Data Mining Algorithms for Massive Data Sets

2014 ◽  
Vol 556-562 ◽  
pp. 3901-3904
Author(s):  
Cui Xia Tao
Keyword(s):  
Data Mining ◽  
Data Sets ◽  
Modern Information Technology ◽  
Incremental Processing ◽  
Data Mining Algorithms ◽  
High Efficient ◽  
Data Volume ◽  
Efficient Data ◽  
Valid Solution ◽  
Mining Algorithms

Data mining means to extract information and knowledge that potentially useful while still unknown in advance, from a large quantity of implicit incomplete, random data. With the quick advancement of modern information technology, people are accumulating data volume on the increase sharply, often at the speed of TB. How to extract meaningful information from large amounts of data has become a big problem must be tackled. In view of the huge amounts of data mining, distributed parallel processing and incremental processing is valid solution.

A Taxonomy of Data Mining Problems

2018 ◽  
Vol 5 (2) ◽  
pp. 73-86 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Real Life ◽  
The Body ◽  
Real World Data ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Body Of Knowledge ◽  
Efficient Data ◽  
Mining Algorithms ◽  
Made In

Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data mining problems. But there is no single source that identifies which techniques solve what problems and how, the advantages and limitations, and real-life use-cases. Lately, identifying data mining techniques and corresponding problems that they solve has drawn significant attention. In this paper, the author describes the progress made in developing data mining techniques and then classify them in terms of data mining problems taxonomy to help assist practitioners in using appropriate data mining techniques that solve business problems. This will allow researchers to expand the body of knowledge in this discipline. This article proposes a data mining problems taxonomy based on data mining techniques being used. Prominent data mining problems include classification, optimization, prediction, partitioning, relationship, pattern matching, recommendation, ranking, sequential patterns and anomaly detection. The data mining techniques that are used to solve these data mining problems in general fall under top 10 data mining algorithms.

scholarly journals A Visual Framework for Spatial Data Mining

2015 ◽  
pp. 67
Author(s):  
Nguyen Vinh Nam ◽  
Le Hoai Bac
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Spatial Query ◽  
Data Mining Technique ◽  
Access Methods ◽  
New Methods ◽  
Challenges And Opportunities ◽  
Efficient Data ◽  
Spatial Access Methods

The  unique properties of spatial data provide challenges  and  opportunities  for  researching  new methods  in  spatial  data  mining.  In  this  article,  we propose  an  interoperable  framework  that  integrates Geographic  Information  System  (GIS)  with  the  spatial data  mining  processto  facilitate  spatial  data preparation,  to  extract  spatial  relationships  that  can take  advantage of traditional data  mining toolkits such as Weka, and to reveal significant spatial patterns. With this approach, it’svery straightforward to adopt spatial access methods and spatial query processing algorithms foran  efficient  data  mining  technique.  Moreover,  our framework  visually  supports  the  complete  spatial  data mining process.

A Taxonomy of Data Mining Problems

2020 ◽  
pp. 512-528
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Real Life ◽  
The Body ◽  
Real World Data ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Body Of Knowledge ◽  
Efficient Data ◽  
Mining Algorithms ◽  
Significant Attention

Much of the research in data mining and knowledge discovery has focused on the development of efficient data mining algorithms. Researchers and practitioners have developed data mining techniques to solve diverse real-world data mining problems. But there is no single source that identifies which techniques solve what problems and how, the advantages and limitations, and real-life use-cases. Lately, identifying data mining techniques and corresponding problems that they solve has drawn significant attention. In this paper, the author describes the progress made in developing data mining techniques and then classify them in terms of data mining problems taxonomy to help assist practitioners in using appropriate data mining techniques that solve business problems. This will allow researchers to expand the body of knowledge in this discipline. This article proposes a data mining problems taxonomy based on data mining techniques being used. Prominent data mining problems include classification, optimization, prediction, partitioning, relationship, pattern matching, recommendation, ranking, sequential patterns and anomaly detection. The data mining techniques that are used to solve these data mining problems in general fall under top 10 data mining algorithms.

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