scholarly journals Data mining as a cognitive tool: Capabilities and limits

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Maxim Polyakov ◽  
Igor Khanin ◽  
Gennadiy Shevchenko ◽  
Vladimir Bilozubenko
Keyword(s):  
Data Mining ◽  
Scientific Method ◽  
Cognitive Tool ◽  
Scientific Cognition ◽  
Use Of Data ◽  
Cognitive Levels ◽  
New Knowledge ◽  
Empirical Regularity ◽  
The Relationship

Due to the large volumes of empirical digitized data, a critical challenge is to identify their hidden and unobvious patterns, enabling to gain new knowledge. To make efficient use of data mining (DM) methods, it is required to know its capabilities and limits of application as a cognitive tool. The paper aims to specify the capabilities and limits of DM methods within the methodology of scientific cognition. This will enhance the efficiency of these DM methods for experts in this field as well as for professionals in other fields who analyze empirical data. It was proposed to supplement the existing classification of cognitive levels by the level of empirical regularity (ER) or provisional hypothesis. If ER is generated using DM software algorithm, it can be called the man-machine hypothesis. Thereby, the place of DM in the classification of the levels of empirical cognition was determined. The paper drawn up the scheme illustrating the relationship between the cognitive levels, which supplements the well-known schemes of their classification, demonstrates maximum capabilities of DM methods, and also shows the possibility of a transition from practice to the scientific method through the generation of ER, and further from ER to hypotheses, and from hypotheses to the scientific method. In terms of the methodology of scientific cognition, the most critical fact was established – the limitation of any DM methods is the level of ER. As a result of applying any software developed based on DM methods, the level of cognition achieved represents the ER level.

Predicting effects of selected impregnation processes on the observed bending strength of wood, with use of data mining models

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 4891-4904
Author(s):  
Selahattin Bardak ◽  
Timucin Bardak ◽  
Hüseyin Peker ◽  
Eser Sözen ◽  
Yildiz Çabuk
Keyword(s):  
Data Mining ◽  
Goodness Of Fit ◽  
Bending Strength ◽  
Wood Preservation ◽  
Ambient Conditions ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
The Cost ◽  
Mining Algorithms ◽  
The Relationship

Wood materials have been used in many products such as furniture, stairs, windows, and doors for centuries. There are differences in methods used to adapt wood to ambient conditions. Impregnation is a widely used method of wood preservation. In terms of efficiency, it is critical to optimize the parameters for impregnation. Data mining techniques reduce most of the cost and operational challenges with accurate prediction in the wood industry. In this study, three data-mining algorithms were applied to predict bending strength in impregnated wood materials (Pinus sylvestris L. and Millettia laurentii). Models were created from real experimental data to examine the relationship between bending strength, diffusion time, vacuum duration, and wood type, based on decision trees (DT), random forest (RF), and Gaussian process (GP) algorithms. The highest bending strength was achieved with wenge (Millettia laurentii) wood in 10 bar vacuum and the diffusion condition during 25 min. The results showed that all algorithms are suitable for predicting bending strength. The goodness of fit for the testing phase was determined as 0.994, 0.986, and 0.989 in the DT, RF, and GP algorithms, respectively. Moreover, the importance of attributes was determined in the algorithms.

scholarly journals DATA MINING TECHNIQUES FOR EDUCATIONAL DATA: A REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 166-177 ◽  
Author(s):  
Pragati Sharma ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Educational Institutions ◽  
Data Set ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
New Knowledge ◽  
Hidden Patterns ◽  
Educational Field

Recently, data mining is gaining more popularity among researcher. Data mining provides various techniques and methods for analysing data produced by various applications of different domain. Similarly, Educational mining is providing a way for analyzing educational data set. Educational mining concerns with developing methods for discovering knowledge from data that come from educational field and it helps to extract the hidden patterns and to discover new knowledge from large educational databases with the use of data mining techniques and tools. Extracted knowledge from educational mining can be used for decision making in higher educational institutions. This paper is based on literature review of different data mining techniques along with certain algorithms like classification, clustering etc. This paper represents the effectiveness of mining techniques with educational data set for higher education institutions.

Use of data mining in the corrosion classification of pipelines in Naphtha Hydro-Threating Unit (NHT)

2019 ◽  
Vol 3 (1) ◽  
pp. 14-21 ◽  
Author(s):  
Amir Samimi ◽  
◽  
Pathmanathan Rajeev ◽  
Ali Bagheri ◽  
Ali Nazari ◽  
...  
Keyword(s):  
Data Mining ◽  
Use Of Data

The Association Analysis of Technical Indicators of Shanghai Stock Market

2014 ◽  
Vol 651-653 ◽  
pp. 1651-1654
Author(s):  
Rui Zhong Wang
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Stock Market ◽  
Success Rate ◽  
Association Analysis ◽  
Association Rules ◽  
Technical Indicators ◽  
Use Of Data ◽  
Stock Market Investors ◽  
The Relationship

This paper selected as part of a number of technical indicators, the main use of data mining software for different technical indicators signal given trading technical analysis of association rules. By studying the resulting characteristics of the relationship between the rules and give the stock market investors a certain decision support, to enable investors to operate with a higher success rate.

scholarly journals Teknik Bagging Dan Boosting Pada Algoritma CART Untuk Klasifikasi Masa Studi Mahasiswa

2019 ◽  
Vol 5 (1) ◽  
pp. 21-30
Author(s):  
Ahmad Rusadi Arrahimi ◽  
Muhammad Khairi Ihsan ◽  
Dwi Kartini ◽  
Mohammad Reza Faisal ◽  
Fatma Indriani
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Undergraduate Students ◽  
New Knowledge ◽  
Testing Data ◽  
Academic Information ◽  
Using Data ◽  
Boosting Technique ◽  
Cart Algorithm

Undergraduate Students data in academic information systems always increases every year. Data collected can be processed using data mining to gain new knowledge. The author tries to mine undergraduate students data to classify the study period on time or not on time. The data is analyzed using CART with bagging techniqu, and CART with boosting technique. The classification results using 49 testing data, in the CART algorithm with bagging techniques 13 data (26.531%) entered into the classification on time and 36 data (73.469%) entered into the classification not on time. In the CART algorithm with boosting technique 16 data (32,653%) entered into the classification on time and 33 data (67,347%) entered into the classification not on time. The accuracy value of the classification of study period of undergraduate students using the CART algorithm is 79.592%, the CART algorithm with bagging technique is 81.633%, and the CART algorithm with boosting technique is 87.755%. In this study, the CART algorithm with boosting technique has the best accuracy value.

scholarly journals R&D Subsidies as Drivers of Corporate Performance in Slovenia: The Regional Perspective

2017 ◽  
Vol 8 (2) ◽  
pp. 79-95 ◽  
Author(s):  
Dejan Ravšelj ◽  
Aleksander Aristovnik
Keyword(s):  
Economic Growth ◽  
Corporate Performance ◽  
Positive Impact ◽  
Vital Role ◽  
Role Of Government ◽  
New Knowledge ◽  
The Relationship ◽  
Unique Dataset

AbstractInvestment in research and development (R&D) plays a vital role in economic growth. Therefore, the crucial role of government is to encourage companies to develop new knowledge, skills, and innovations in order to achieve greater competitiveness, employment creation, and economic development. The aim of this paper is to determine whether R&D subsidies contribute to corporate performance and ascertain whether the relationship between the amount of R&D subsidies and corporate performance is moderated by Slovenian cohesion (NUTS 2 level) and statistical (NUTS 3 level) regions. This paper ultimately tries to classify statistical regions within meaningful groups. Using an OLS regression, a unique dataset of 407 Slovenian companies is analysed for 2014. The empirical results reveal that R&D subsidies have a positive impact on corporate performance and confirm that cohesion and statistical regions can moderate the effect of R&D subsidy on corporate performance. Moreover, the paper provides for the classification of Slovenian statistical regions into four groups.

DATA MINING KLASTERISASI PENJUALAN ALAT-ALAT BANGUNAN MENGGUNAKAN METODE K-MEANS (STUDI KASUS DI TOKO ADI BANGUNAN)

2018 ◽  
Vol 1 (2) ◽  
pp. 83-91
Author(s):  
M. Hasyim Siregar
Keyword(s):  
Data Mining ◽  
Cost Reduction ◽  
Building Materials ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Operational Cost ◽  
Use Of Data

In the world of business competition today, we are required to continually develop business to always survive in the competition. To achieve this there are a few things that can be done is to improve the quality of the product, adding the type of product and operational cost reduction company with how to use data analysis of the company. Data mining is a technology that automate the process to find interesting patterns and sensitive from the large data sets. This allows human understanding about finding patterns and scalability techniques. The store Adi Bangunan is a shop which is engaged in the sale of building materials and household who have such a system on supermarket namely buyers took own goods that will be purchased. Sales data, purchase goods or reimbursed some unexpected is not well ordered, so that the data is only function as archive for the store and cannot be used for the development of marketing strategy. In this research, data mining applied using the model of the process of K-Means that provides a standard process for the use of data mining in various areas used in the classification of because the results of this method can be easily understood and interpreted.

From data mining to wisdom mining

2021 ◽  
pp. 016555152110308
Author(s):  
Salma Khan ◽  
Muhammad Shaheen
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Real World ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Comprehensive Literature Review ◽  
Wise Decision ◽  
Mining Algorithms ◽  
The Relationship ◽  
Real World Problems

The knowledge gained from data mining is highly dependent on the experience of an expert for further analysis to increase effectiveness and wise decision-making. This mined knowledge requires actionability enhancement before it can be applied to real-world problems. The literature highlights the reasons that emerged the need to incorporate human wisdom in decision-making for complex problems. To solve this problem, a domain called ‘Wisdom Mining’ is recommended, proposing a set of algorithms parallel to the algorithms proposed by the data mining. In wisdom mining, a process to extract wisdom needs to be defined with less influence from an expert. This review proposed improvements to data mining techniques and their applications in the real world and emphasised the need to seek ways to harness wisdom from data. This study covers the diverse definitions and different perspectives of wisdom within philosophy, psychology, management and computer science. This comprehensive literature review served as a foundation for constructing a wise decision framework that aided in identifying the wisdom factors like context, utility, location and time. The inclusion of these wisdom factors in existing data mining algorithms makes the transition from data mining to wisdom mining possible. This research includes the relationship between these two mining process that facilitated further elucidation of the wisdom mining process. Potential research trends in the domain are also seen as a potential endeavour to improve the analysis and use of data.

Investment Traits and Intelligence in Adulthood

2013 ◽  
Vol 34 (2) ◽  
pp. 82-89 ◽  
Author(s):  
Sophie von Stumm
Keyword(s):  
Individual Differences ◽  
Personality Traits ◽  
Fluid Intelligence ◽  
Need For Cognition ◽  
Openness To Experience ◽  
Intelligence Tests ◽  
Crystallized Intelligence ◽  
New Knowledge ◽  
The Relationship ◽  
Over Time

Intelligence-as-knowledge in adulthood is influenced by individual differences in intelligence-as-process (i.e., fluid intelligence) and in personality traits that determine when, where, and how people invest their intelligence over time. Here, the relationship between two investment traits (i.e., Openness to Experience and Need for Cognition), intelligence-as-process and intelligence-as-knowledge, as assessed by a battery of crystallized intelligence tests and a new knowledge measure, was examined. The results showed that (1) both investment traits were positively associated with intelligence-as-knowledge; (2) this effect was stronger for Openness to Experience than for Need for Cognition; and (3) associations between investment and intelligence-as-knowledge reduced when adjusting for intelligence-as-process but remained mostly significant.

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