On Approach for the Implementation of Data Mining to Business Process Optimisation in Commercial Companies

Pick, Aleksander; Vasilecas, Olegas; Kalibatiene, Diana; Rupnik, Rok

doi:10.3846/20294913.2013.796501

Cited by 9 publications

(9 citation statements)

References 21 publications

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“…They provide for each study the data source, the financial ratios used, the country of origin and the data collection period. Pivk et al (2013) apply Data Mining techniques in order to improve business processes in banking, telecom and retail sectors. While still requiring a lot of coordination and manual adjustment, the proposed Data Mining solution is succesfully applied in eight commercial companies.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Two-Stage Methodology for Optimising the Accuracy of Performance Classification Models

Costea

Феррара

Şerban

2017

Technological and Economic Development of Economy

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In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: “good”, “average”, “poor” performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available.

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Section: Introductionmentioning

confidence: 99%

An Integrated Two-Stage Methodology for Optimising the Accuracy of Performance Classification Models

Costea

Феррара

Şerban

2017

Technological and Economic Development of Economy

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“…In this study, we applied a cross-industry standard process for data mining (CRISP-DM) framework to facilitate our research. This framework is a data mining process model that is open-sourced and well-grounded [31]. CRISP-DM provides industrial independence [32].…”

Section: Application Of the Crisp-dm To Applied It Problemmentioning

confidence: 99%

“…This framework allows businesses to safeguard their systems from service interruptions instigated by a DDoS attack [5]. CRISP-DM contains six phases of business understanding, data assessment, data preparation, modeling, evaluation, and deployment [31][32][33]. These phases are explained below.…”

Section: Application Of the Crisp-dm To Applied It Problemmentioning

confidence: 99%

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Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Zeinalpour,

McElroy

2024

Electronics

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Distributed Denial of Service (DDoS) attacks have increased in frequency and sophistication over the last ten years. Part of the challenge of defending against such attacks requires the analysis of very large volumes of data. Metaheuristic algorithms can assist in selecting relevant features from the network traffic data for use in DDoS detection models. By efficiently exploring different combinations of features, these methods can identify subsets that are informative for distinguishing between normal and attack traffic. However, identifying an optimized solution in this area is an open research question. Tuning the parameters of metaheuristic search techniques in the optimization process is critical. In this study, a switching approximation is used in a variety of metaheuristic search techniques. This approximation is used to find the best solution for the analysis of the network traffic features in either lower or upper values between 0 and 1. We compare the fine-tuning of this parameter against standard approaches and find that it is not substantially better than the BestFirst algorithm (a standard default approach for feature selection). This study contributes to the literature by testing and eliminating various fine-tuning strategies for the metaheuristic approach.

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“…Data mining can support the process management, process reengineering, and the process improvement methodologies. Pivk, Vasilecas, Kalibatiene, and Rupnik () expressed that with the aid of data mining, knowledge can be moved between the various departments and the processes in organizations. Consequently, important processes can be identified and the information flow of processes can be optimized (Zhonghua & Limei, ).…”

Section: Introductionmentioning

confidence: 99%

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

Khanbabaei

Alborzi

Sobhani

et al. 2019

Knowl Process Manag

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Processes as one of the valuable knowledge resources can create sustainable competitive advantages in organizations. There is a large number of processes in organizations. They generate a high volume of process data that leads to the high‐dimensionality problems, complex relationships, dynamic changes, and difficulties in the understanding of the process by human resources. Traditional process improvement methodologies have weaknesses in environment with the large number of processes. Data mining techniques can support process improvement in this environment. They can recommend the improvement suggestions through extracting valuable patterns from a high volume of the process dataset. Recently, knowledge‐intensive processes have been increasingly concentrated in the field of process improvement. These types of processes can induce a competitive behavior over the other processes. The main problem is the improvement of competitive and knowledge‐intensive processes in a high volume of process dataset. The main purpose of this paper is to present a model to identify the behavior of competitive and knowledge‐intensive processes and recommend improvement suggestions. For this purpose, data mining techniques are applied to extract valuable patterns hidden in a high volume of process dataset. In this regard, K‐means clustering and C5 classification algorithms are applied to extract valuable patterns. A real process dataset was used to evaluate the effectiveness and applicability of the model. The results confirmed that the proposed model can apply data mining techniques to support competitive and knowledge‐intensive process improvement in a high volume of process dataset.

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On Approach for the Implementation of Data Mining to Business Process Optimisation in Commercial Companies

Cited by 9 publications

References 21 publications

An Integrated Two-Stage Methodology for Optimising the Accuracy of Performance Classification Models

An Integrated Two-Stage Methodology for Optimising the Accuracy of Performance Classification Models

Comparing Metaheuristic Search Techniques in Addressing the Effectiveness of Clustering-Based DDoS Attack Detection Methods

Applying clustering and classification data mining techniques for competitive and knowledge‐intensive processes improvement

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