Design and Implementation of Office Management System in Small and Medium Sized Enterprises Based on Data Mining Technology

This paper introduces data mining technology in enterprise competitive intelligence system; and then introduced theoretical foundation and main clustering method of cluster analysis. The article emphasis on the FCM algorithm and principle and described implementation steps, and proposed the improvement FCM algorithm based on K mean particle size; finally, realize the design and implementation of enterprise competitive intelligence analysis and mining service system, and the improved FCM algorithm is applied in the system.

Enterprise Competitive Intelligence System Research Based on Data Mining Technology

Under the background of current continuous changing market economy and rapid development of information, enterprise will increase the demand of information or data, and correspondingly put forward higher requirements for its quality. For the purpose of better usage and information data control, enterprises put forward pioneering construct competitive intelligence system. Competitive intelligence system can provide accurate, effective and high value, timeliness strong intelligence data to enterprises and the enterprises may adjust their competitive strategy, and effectively enhance their own competitive advantages. The application of data mining technology in enterprise competitive intelligence system can upgrade information reserve capacity and optimize information channels to improve system efficiency effectively. In the process of enterprises development, the establishment of competitive intelligence system based on data mining technology has particularly important practical significance, so we should put more effort into establishing and running the intelligence system.

The Enterprise Competitive Intelligence System Based on Data Mining

In knowledge economy era, data and information become important economy resources. Drawing valuable information quickly from great amount of datum and reacting immediately, will become the key of corporation success. The paper introduces Competitive Intelligence and Corporation Competitive Intelligence System (CIS) firstly. Then, the paper gives the concept of Data Mining and general process. Furthermore, the paper gives a model of Corporation Competitive Intelligence System based on data mining and its relevant process.

Research and Construction of the Enterprise Competitive Intelligence System

In the era of rapid growth and high competition, a company must possess an information/knowledge advantage in order to hold the upper hand in the industry. Therefore, the company has to continuously monitor its competitors in order to get enough information and convert the information into competitive knowledge. Although information technology has been used in many areas and has many successful examples, it is rarely the case that information technology was used for the task of competitor intelligence. Accordingly, this study describes the Enterprise Competitive Intelligence System. Competitive Intelligence (CI) is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. Competitive Intelligenceaims to monitor a firms external environment for information relevant to its decision-making process. The article describes the concept and the functions of the Enterprise Competitive Intelligence System, discusses the principle of its construction, and also discusses the construction process and the awareness of CI.

Foundations of Competitive Intelligence System to form Business Coalitions

This study shows how a business can identify the networks allowing to form coalitions to obtain French procurement contracts. To this end, we have represented, by a graph, the 2008 co-branding system. We have detected, in this graph, 1360 strategic networks of which the organization reveals, on the one hand, identical characteristics within business networks, and on the other hand, the role of the dominant parties as to access to industrial labor. From these results, we propose a network cartography allowing us to consider new applications for competitive intelligence.