Data Mining Based Decision Support Research Articles

Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm

Data mining is widely used in engineering and science, On the contrary, it is used in finance and marketing applications to resolve the challenges in the respective fields. Data mining based decision support system enhances the organization performance by analysing the ground reality. Turbulent economy is common for every organization due to the competition, cost, tax pressures, etc., Privatization, Globalization and liberalization drags the organization more into a competitive environment. In order to balance the competition and withstand to achieve desired gain proper marketing strategies are need to planned and executed. Marketing decision support system helps to reduce the organization burdens in analysing and strategical planning through its efficient data mining approach. This research work proposed a data mining based decision support system using decision tree and artificial neural network as a hybrid approach to estimate the marketing strategies for an organization.

Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm

Data mining is widely used in engineering and science, On the contrary, it is used in finance and marketing applications to resolve the challenges in the respective fields. Data mining based decision support system enhances the organization performance by analysing the ground reality. Turbulent economy is common for every organization due to the competition, cost, tax pressures, etc., Privatization, Globalization and liberalization drags the organization more into a competitive environment. In order to balance the competition and withstand to achieve desired gain proper marketing strategies are need to planned and executed. Marketing decision support system helps to reduce the organization burdens in analysing and strategical planning through its efficient data mining approach. This research work proposed a data mining based decision support system using decision tree and artificial neural network as a hybrid approach to estimate the marketing strategies for an organization.

Graph embedded rules for explainable predictions in data streams

Understanding the reason why a prediction has been made by a machine is crucial to grant trust to a human decision-maker. However, data mining based decision support systems are, in general, not designed to promote interpretability; instead, they are developed to improve accuracy. Interpretability becomes a more challenging issue in the context of data stream mining. Where the prediction model has to deal with enormous volumes of data gathered continuously at a fast rate and whose underlying distribution may change over time. On the one hand, the majority of the methods that address classification in a data stream are black-box models or white-box models into ensembles. Either do not provide a clear view of why a particular decision has been made. On the other hand, white-box models, such as rule-based models, do not provide acceptable accuracy to be considered in many applications. This paper proposes modeling the data as a special graph, which is built over the attribute space, and from which interpretable rules can be extracted. To overcome concept drift and enhance model accuracy, different variants of such graphs are considered within an ensemble that is updated over time. The proposed approach has shown the best overall classification results when compared to six rule-based algorithms in twelve streaming domains.

A Datamining Based Decision Support System For Fruit Manufacturing

Agricultural activities provide one of the main sources of living in many regions of the world. Such products play significant roles for the survival of living organisms. Farmers all around the world work towards reaching their economic goals through agricultural activities. By doing this, they invest both their capital and time. In some occasions, such investments end up with serious losses and planted products either grow in low levels or do not grow at all. In this study, in order to avoid such situations and guide the investors intelligently, a decision support system based on decision tree is developed. Related parameters in accordance with the climatic and geographic characteristics of the region are determined within this proposed system. Then, appropriate types of fruits are proposed to the users as the outcome of the system based on these parameters. By doing this, producing more efficient and profitable products is aimed.

Health services in smart cities: Choosing the big data mining based decision support

ABSTRACTWith the rapid increase in technology, the healthcare sector is approaching towards digital solutions for clinical evidence related to various diseases. This research paper provides a theoretical framework for a Clinical Decision Support System with the help of Software as Medical Device regime (CDS-SaMD). The proposed software model comprises layered architecture with each layer acquiring and processing data obtained through Electronic Health Record (EHR) in hospitals. The layers of the model involve sensors for measuring data, pre-processing, transmission to the cloud servers for storage, data reduction, feature extraction, and decision trees. The overall model assists doctors in deciding on diagnosis and treatment of diseases in an accurate and fast manner as compared to the conventional diagnostic procedures. The software is implemented in smartphones and is helpful in cities for diagnosis and treatment of chronic disease like heart attacks and hypertension. The proposed model will help in reducing the mortality due to chronic diseases and will improve facilities for patients’ care.

A semi-automatic planning BOM generator based on VMI forecasting

ABSTRACTVendor-managed inventory (VMI) is a case of supply chain management (SCM) approaches in which the customer of a product provides certain information to a vendor of that product and the vendor takes full responsibility for maintaining an agreed inventory of the material, usually at the customer’s consumption location or store. The function of a computerised bill of materials (BOM) module plays an important role in supporting VMI collaborations. This paper presents a VMI case study, identifies the side effects by the VMI collaboration and proposes a data mining-based decision support module to generate planning BOM. It is an embed module for an enterprise resource planning (ERP) system to reduce those risks caused via VMI collaboration. A real-world industry case study is utilised to demonstrate the merits of the proposed approach.

Towards data mining based decision support in manufacturing maintenance

The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.

Visual Knowledge Generation from Data Mining Patterns for Decision-Making

The visual data mining based decision support systems had already been recognized in literature. It allows users analysing large information spaces to support complex decision-making. Prior research provides frameworks focused on simply representing extracted patterns. In this paper, we present a new model for visually generating knowledge from these patterns and communicating it for intelligent decision-making. To prove the practicality of the proposed model, it was applied in the medical field to fight against nosocomial infections in the intensive care units.

Lloyd and Minkowski Based K-Means Clustering for Effective Diagnosis of Heart Disease and Stroke

Computer Aided Data mining based decision support system plays a major role in research to easily diagnose medical disease at an early stage. Automatic annotation approach grouped similar medical semantic terms, however, false detection lead to incorrect merging increasing the computational complexity. In this work, a method called Lloyd and Minkowski based K-Means Clustering (LMK-MC) is designed to organize various features in heart disease, with the aim of increasing the features to be incorporated improving the clustering efficiency and reducing the average computational complexity. The Lloyd and Minkowski based K-Means Clustering includes a function that performs a Minkowski based K-Means Clustering to organize similar type of symptom features for easy prediction. By applying Minkowski based K-Means Clustering, similar type of features causing heart disease is analyzed for any set of k centers, aiming at improving the clustering efficiency. Next, the method LMK-MC, applies pair-wise proximities between all pairs of features reducing the computational complexity for intra-cluster. Finally, Lloyd's algorithm based clustering on medical data is designed that moves every center point to the centroid and performs updates. Lloyd's algorithm based clustering obtains local minima solution that easily merges similar features leading to heart disease and strokes, various disease features for labeling is identified effectively with high recognition accuracy. In order to measure the efficiency of LMK-MC, experiments are conducted using Cleveland Clinic Foundation Heart disease data set. Experimental results demonstrate that the proposed Lloyd and Minkowski based K-Means Clustering achieves efficient amount of identification improving the clustering efficiency, reducing the computational complexity while measuring intra-cluster distances for different clusters and, heart disease and stroke identification rate with minimum processing time.

A self-organizing feature maps and data mining based decision support system for liability authentications of traffic crashes

This study develops a decision support tool for liability authentications of two-vehicle crashes based on generated self-organizing feature maps (SOM) and data mining (DM) models. Factors critical to liability attributions commonly identified theoretically and practically were first selected. Both SOM and DM models were then generated for frontal, side, and rear collisions of two-vehicle crashes. Appropriateness of all generated models was evaluated and confirmed. Finally, a decision support tool was developed using active server pages. Although with small data size, the decision support system was considered capable of giving reasonably good liability attributions and references on given cases.

Agent and data mining based decision support system and its adaptation to a new customer-centric electronic commerce

Abstract Recently, as the Internet has become more widely used, Electronic Commerce (EC) has emerged and has developed a high-level business environment. The customer-centric EC model is important for the success of EC and this study presents a new customer-centric EC model in make-to-order (MTO) semiconductor manufacturing environment. In this study we proposed the EC model providing the process transparency of process sampling method that can provide online semiconductor customers with the performance information of available process sampling methods which can be used at all manufacturing process steps for their own products in MTO manufacturing environment, and then the capability to select a desirable one among them based on their purchase situations on EC web site. In the proposed EC model the customer can select a process sampling method that is most suitable to him/her according to the customer's purchase situation. In this model the use of intelligent decision support system called customized sampling decision support system (CSDSS) that can autonomously generate available customized sampling methods and provide the performance information of those methods to EC system is requisite. We implemented an Internet-based prototype of CSDSS which had an architecture based on intelligent agent technology and also the successful integration of data mining process for the generation of optimal sampling method into DSS framework by means of applying that technology.