Marketing Decision Support System Based on Data Mining Technology

It is very hard to acquire the information what you really want with the accumulation of large amount of data. Data mining technology has been achieved a great progress with the rapid development of computer science, artificial intelligence, and warehouse. City planning is a job with strong subjective which promotes the development of digital city planning. This makes an increasing demand of data mining technology in areas of digital city planning. This paper described the history and theory development of data mining technologies, especially, rough set whose chief advantage is that it does not need any preliminary or additional information about data. Then some successful applications of rough set were introduced. Furthermore, merits and shortages of some data mining techniques such as rough sets, fuzzy sets, and neural networks were discussed. Finally, the problems faced by city designers were addressed and the possible resolution was also presented.

Hierarchical scaling of marketing decision support systems

As a confluence of data mining and World Wide Web technologies, it is now possible to perform data mining on Web log records collected from the Internet Web-page access history. The behaviour of Web page readers is imprinted in the Web server log files. Analyzing and exploring regularities in this behaviour can improve the system performance, enhance the quality and delivery of Internet information services to the end user, and identify populations of potential customers for electronic commerce. Thus, by observing people using collections of data, data mining can bring a considerable contribution to digital library designers. In a joint effort between the TeleLearning-NCE (Networks of Centres of Excellence) project on the Virtual University and the NCE-IRIS project on data mining, we have been developing a knowledge discovery tool, called WebLogMiner, for mining Web server log files. This paper presents the design of WebLogMiner, reports current progress and outlines future work in this direction.

Discusses the application of decision support systems (DSS) to assist in solving marketing decisions. As the marketing environment becomes more competitive the pressure to gain the “edge” over your competitors becomes more intense. Marketing decision support systems (MDSS) are valuable tools to assist in making marketing decisions to do just that! MDSS can be used to support, rather than replace, decision makers in the complex, semi or unstructured situations which are common in marketing problems. They incorporate the personal judgement and experience of the user to improve the effectiveness, rather than the efficiency of decision making. This means that managers will have more “quality” time to spend on analysis and planning for the future, rather than merely reflecting on the past. Discusses different types of DSS within an environment where there are discrepancies over definition, jargon and suppliers’ claims.

Marketing decision makers are confronted with an increasing amount of information. This leads to a complex decision environment that may cause decision makers to lapse into using mental-effort-reducing heuristics such as anchoring and adjustment. In an experimental study, we find that the use of a marketing decision support system (MDSS) increases the effectiveness of marketing decision makers. An MDSS is effective because it assists its users in identifying the important decision variables and, subsequently, making better decisions based on those variables. Decision makers using an MDSS are also less susceptible to applying the anchoring and adjustment heuristic and, therefore, show more variation in their decisions in a dynamic environment. Low-analytical decision makers and decision makers operating under low time pressure especially benefit from using an MDSS.

This paper expounds the fundamental theory of data mining and customer segmentation, and studies on the specific implementation of data mining processes and technology. We put forward the customer segmentation application model based on data mining technology, which is applied in the retailing industry to carry out a empirical study.

The paper introduces a "lazy" Data Mining technology, which models students' learning characteristics by considering real data instead of deriving ("guessing") their characteristics explicitly. In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimal learning success in terms of the best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. Here, we introduce a complementary technology to mine course characteristics similarities of former students' study traces and utilize them to optimize curricula of current students based to their performance traits revealed by their study achievements so far. This way, technology generates suggestions of personalized curricula. Furthermore, this technology is supplemented by an adaptation mechanism, which compares recent data with historical data to ensure that the similarity of mined characteristics follow the dynamic changes affecting curriculum (e.g., revision of course contents and materials, and changes in teachers, etc.).

The purpose is to enhance the timeliness of news, provide real-time news for the audience, and attract more traffic for the news media, thereby helping news media grow financially and strengthening the network security communication efficiency and accuracy. First, an intelligent real-time image acquisition system is introduced, and its principle and advantages are analyzed. Second, an intelligent real-time image acquisition model based on DL (Deep Learning) algorithm is established by combining DM (Data Mining) technology. Finally, the application of an intelligent image acquisition system in network communication is adjusted. The research results show that the combination of DL intelligent real-time image acquisition system with DM technology can effectively deal with various network communication scenarios, enhancing the accuracy and timeliness of network communication. The AlexNet model performs excellently in all aspects, with or without data enhancement or equalization. The recognition rate reaches 99.6% after data equalization and enhancement, and the AlexNet model can accurately classify news and non-news sceneries. This shows that the application of an intelligent real-time image acquisition system based on DM technology can effectively enhance the real-time performance of network communication. The results set an example for the application of relevant intelligent image acquisition technology in the journalism industry.

In recent years, database technology has improved significantly, and database management systems have gained widespread adoption. As a result, the volume of data saved across numerous databases has increased exponentially. However, the vast majority of information is hidden beneath this mountain of data. The goal of this study is to get a comprehensive understanding of the decision information system employed in the Internet of Things for intelligent manufacturing data processing. The proposed Decision support system (DSS) information processing is accomplished through the use of an IoT-based intelligent manufacturing data mining model. Numerous DM algorithms that are frequently encountered are analyzed, including the ARS and Apriori Algorithm (AA). The Decision Tree data mining algorithm is investigated, as is the generation of several Decision Trees and the pruning algorithm for digital twins. The findings demonstrate that data mining technology is capable of analyzing statistical data from a variety of angles and perspectives by modeling, classifying, and grouping large amounts of data as well as discovering correlations between them. Additionally, statistical work involves the calculation of data and the use of their correlations to aid in decision analysis. The proposed theoretical framework demonstrates how DSS-integrated components can work cooperatively in Intelligent Manufacturing to define a stable data flow within the Internet of Things. Particular emphasis is placed on conceptualizing the decision support system's integrated performance.

Little is known about the extent to which the Marketing Decision Support System (MDSS) technology is currently used in Australia, or about the scope for the technology in Australia towards the year 2000. This paper reports the results of recent survey research into MDSS in Queensland by industry sector (agriculture and mining, manufacturing, construction, and services). The results suggest that there is an urgent need to boost the pace of MDSS development in all industry sectors, and that this should be given a high priority in government policy initiatives to enhance Australia's competitive advantage. It is possible, otherwise, that the present gap in information usage between Australia and its competitors will widen, with consequent negative implications for the nation's current account deficit, foreign debt and unemployment.

Marketing decision support systems: Adoption, use, and satisfaction

The thesis starts from the basic theory, introduces the method of physical data acquisition and the concept of data mining. Marketing evolves from traditional marketing schemes to data mining, which leads to the relationship between data mining technology and modern market management. It focuses on the method of obtaining user information and data mining technology in "cross-marketing", "integrated marketing", and "integrated marketing". Applications in "real-time marketing", "green marketing", "relationship marketing", and "network marketing". At the same time, combined with case analysis, the comprehensive application of data mining in "shopping basket analysis" is discussed and elucidated from the theoretical and technical level.

With the continuous development of data mining technology, to apply the data mining techniques to transportation sector will provide service to transportation scientifically and reasonably. In intelligent transportation, the analysis of traffic flow data is very important, how to analyze the traffic data intelligently is more difficult problem, so using a new data mining techniques to replace the traditional data analysis and interpretation methods is very necessary and meaningful, clustering algorithm is the collection of physical or abstracting objects into groups of similar objects from the multiple classes of processes. This paper describes all kinds of the data mining clustering algorithms, clustering algorithm is proposed in the method of dealing with traffic flow data, and applied to the actual traffic data processing, and finally the clustering algorithm is applied to each of highway toll station Various types of car traffic volume data analysis.

Decision support systems (DSS) play a pivotal role as computerized tools, guiding and enhancing decision-making processes vital for organizational progress. This research focuses on developing a system tailored for dynamic decision-making, particularly emphasizing the integration of data mining technology. Decision algorithms and neural networks are discussed in depth, providing a comprehensive understanding of the analytical tools crucial for effective decision support. Additionally, the research sheds light on potential risks, ensuring a nuanced view of challenges that may impact the development of DSS. A significant portion of the study is dedicated to the design of DSS architecture and the strategic integration of data mining within the database. The proposed development stages for a business information system, ranging from feasibility study to release, serve as a structured framework for practical implementation. Details within each stage, including data analysis, cleaning, and module development, are meticulously examined. Emphasis is placed on critical steps such as system design, database design, and extract, transform, load (ETL) process design, elucidating their importance in the holistic development of DSS. The conclusion reinforces the paramount importance of leveraging data mining technology in the process of developing decision support systems.

Students currently mostly experience psychological issues like worry and fear, which are primarily brought on by the high demands placed on them. One psychotherapy technique is music therapy. The goal is to use music to enhance health, particularly as a tool to break down barriers both inside and outside the body. Based on data mining (DM) technologies, this paper examines the impact of music education on students' psychological health. The study demonstrates that the DM algorithm utilised in this work has the lowest error rate, with an average error rate of only 6.90%, followed by the ACA method with an average error rate of 17.48%, and finally the AI algorithm with an average error rate of 29.35%. As can be shown, this approach is more suited to research the effects of music instruction on students' psychological well-being. The functional module based on DM is developed through simulation experiments to confirm the application effectiveness of the DM algorithm. This is done by using the data source of DM and the structural model of the mining system to build this module on the foundation of the original psychological evaluation system.