Data Mining Approach for Knowledge-based Process Planning

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.

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Predictive Temporal Data-Mining Approach for Evolving Knowledge Based Reservoir Operation Rules

Water Resources Management ◽

10.1007/s11269-016-1351-5 ◽

2016 ◽

Vol 30 (10) ◽

pp. 3315-3330 ◽

Cited By ~ 2

Author(s):

S. Mohan ◽

N. Ramsundram

Keyword(s):

Data Mining ◽

Reservoir Operation ◽

Temporal Data Mining ◽

Temporal Data ◽

Knowledge Based ◽

Data Mining Approach ◽

Operation Rules

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A Modern Data-Mining Approach Based on Genetically Optimized Fuzzy Systems for Interpretable and Accurate Smart-Grid Stability Prediction

Energies ◽

10.3390/en13102559 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2559

Author(s):

Marian B. Gorzałczany ◽

Jakub Piekoszewski ◽

Filip Rudziński

Keyword(s):

Data Mining ◽

Smart Grid ◽

Simulated Data ◽

Fuzzy Rule ◽

Stability Prediction ◽

Data Set ◽

Rule Based ◽

Electrical Grid ◽

Knowledge Based ◽

Data Mining Approach

The main objective and contribution of this paper was/is the application of our knowledge-based data-mining approach (a fuzzy rule-based classification system) characterized by a genetically optimized interpretability-accuracy trade-off (by means of multi-objective evolutionary optimization algorithms) for transparent and accurate prediction of decentral smart grid control (DSGC) stability. In particular, we aim at uncovering the hierarchy of influence of particular input attributes upon the DSGC stability. Moreover, we also analyze the effect of possible "overlapping" of some input attributes over the other ones from the DSGC-stability perspective. The recently published and available at the UCI Database Repository Electrical Grid Stability Simulated Data Set and its input-aggregate-based concise version were used in our experiments. A comparison with 39 alternative approaches was also performed, demonstrating the advantages of our approach in terms of: (i) interpretable and accurate fuzzy rule-based DSGC-stability prediction and (ii) uncovering the hierarchy of DSGC-system’s attribute significance.

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