scholarly journals Developing a New Project Evaluation Systems Based on Knowledge

2013 ◽  
Vol 5 (2) ◽  
pp. 59-68 ◽  
Author(s):  
Tadeusz A. Grzeszczyk
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Evaluation System ◽  
Project Evaluation ◽  
Systems Development ◽  
Rule Base ◽  
Data Sets ◽  
Evaluation Systems ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Abstract The article is dedicated to the modelling of a new project evaluation systems based on knowledge. Author suggests possible direction of project evaluation systems development. This enabled the application of data mining algorithms for discovering patterns in data sets. The concept of a new evaluation system based on knowledge is synthetically discussed. The example of using association rule base for analysis of project stakeholders surveys is also presented.

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

Association Rule Mining of Relational Data

2011 ◽  
pp. 87-93
Author(s):  
Anne Denton
Keyword(s):  
Data Mining ◽  
Data Structures ◽  
Association Rule ◽  
Association Rule Mining ◽  
Relational Data ◽  
Rule Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Relational Database Management ◽  
Relational Database Management Systems

Most data of practical relevance are structured in more complex ways than is assumed in traditional data mining algorithms, which are based on a single table. The concept of relations allows for discussing many data structures such as trees and graphs. Relational data have much generality and are of significant importance, as demonstrated by the ubiquity of relational database management systems. It is, therefore, not surprising that popular data mining techniques, such as association rule mining, have been generalized to relational data. An important aspect of the generalization process is the identification of challenges that are new to the generalized setting.

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.

Introduction to Fuzzy Data Mining Methods

2011 ◽  
pp. 55-95 ◽  
Author(s):  
Balazs Feil ◽  
Janos Abonyi
Keyword(s):  
Data Mining ◽  
Model Performance ◽  
Fuzzy Rule ◽  
Classification Problem ◽  
Data Sets ◽  
Rule Based ◽  
Data Mining Algorithms ◽  
Rule Bases ◽  
Mining Algorithms ◽  
Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

scholarly journals A Comparison Study of Data Mining Algorithms for blood Cancer Prediction

passer ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 174-179
Author(s):  
Noor Bahjat ◽  
Snwr Jamak
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Common Disease ◽  
Data Sets ◽  
Blood Cancer ◽  
Cancer Data ◽  
Data Mining Algorithms ◽  
Detection And Diagnosis ◽  
Mining Algorithms ◽  
Early Detection And Diagnosis

Cancer is a common disease that threats the life of one of every three people. This dangerous disease urgently requires early detection and diagnosis. The recent progress in data mining methods, such as classification, has proven the need for machine learning algorithms to apply to large datasets. This paper mainly aims to utilise data mining techniques to classify cancer data sets into blood cancer and non-blood cancer based on pre-defined information and post-defined information obtained after blood tests and CT scan tests. This research conducted using the WEKA data mining tool with 10-fold cross-validation to evaluate and compare different classification algorithms, extract meaningful information from the dataset and accurately identify the most suitable and predictive model. This paper depicted that the most suitable classifier with the best ability to predict the cancerous dataset is Multilayer perceptron with an accuracy of 99.3967%.

scholarly journals Using Data Mining Algorithms to Discover Regular Sound Changes among Languages

2019 ◽  
Vol 292 ◽  
pp. 03018
Author(s):  
Peter Z. Revesz
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Sound Change ◽  
Mining Method ◽  
Data Mining Method ◽  
Data Mining Algorithms ◽  
Sound Changes ◽  
Using Data ◽  
Mining Algorithms

This paper presents a method of using association rule data mining algorithms to discover regular sound changes among languages. The method presented has a great potential to facilitate linguistic studies aimed at identifying distantly related cognate languages. As an experimental example, this paper presents the application of the data mining method to the discovery of regular sound changes between the Hungarian and the Sumerian languages, which separated at least five thousand years ago when the Proto-Sumerian reached Mesopotamia. The data mining method discovered an important regular sound change between Hungarian word initial /f/ and Sumerian word initial /b/ phonemes.

Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽  
2014 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
M. Zainal Mahmudin ◽  
Altien Rindengan ◽  
Winsy Weku
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Large Data ◽  
Apriori Algorithm ◽  
Data Mining Algorithms ◽  
Adequate Information ◽  
Processing Data ◽  
Rule Method ◽  
Mining Algorithms ◽  
Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang  diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari     5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

A Survey on Evolutionary Instance Selection and Generation

2010 ◽  
Vol 1 (1) ◽  
pp. 60-92 ◽  
Author(s):  
Joaquín Derrac ◽  
Salvador García ◽  
Francisco Herrera
Keyword(s):  
Data Mining ◽  
Evolutionary Algorithms ◽  
Nearest Neighbor ◽  
Instance Selection ◽  
Data Sets ◽  
Generation Process ◽  
Data Mining Algorithms ◽  
Instance Generation ◽  
Nearest Neighbor Rule ◽  
Mining Algorithms

The use of Evolutionary Algorithms to perform data reduction tasks has become an effective approach to improve the performance of data mining algorithms. Many proposals in the literature have shown that Evolutionary Algorithms obtain excellent results in their application as Instance Selection and Instance Generation procedures. The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process. It will cover approaches applied to the enhancement of the nearest neighbor rule, as well as other approaches focused on the improvement of the models extracted by some well-known data mining algorithms. Furthermore, some proposals developed to tackle two emerging problems in data mining, Scaling Up and Imbalance Data Sets, also are reviewed.

Trajectory Pattern Mining: Methods and Applications

2014 ◽  
Vol 490-491 ◽  
pp. 1361-1367
Author(s):  
Xin Huang ◽  
Hui Juan Chen ◽  
Mao Gong Zheng ◽  
Ping Liu ◽  
Jing Qian
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Pattern Mining ◽  
Data Sets ◽  
Trajectory Data ◽  
Data Mining Algorithms ◽  
Mining Methods ◽  
Trajectory Pattern ◽  
Mining Algorithms ◽  
Trajectory Pattern Mining

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

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.

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