Research On Competitor Recognition Based On The Integration Of Improved Bacterial Foraging Optimization Algorithm And Clustering Algorithm

Business competitive intelligence analysis problem has always been an important part of business.How to effectively identify competitors and conduct business competitive analysis has been widely concerned by enterprises and relevant scholars.With the development of network and the progress of science and technology,a lot of information about enterprises has been recorded,which provides a way for enterprises and relevant scholars to obtain competitive intelligence information related to competitors in addition to questionnaire survey methods.Because of the convenience and real-time of data acquisition,many scholars try to use network data to do business competitive research for identifying competitors.The main research work of this paper includes the following aspects:(1)A clustering algorithm based on mixed bacteria foraging optimization is proposed.Combining the bacterial foraging optimization algorithm with the cuckoo search algorithm,the problem that the initial clustering center in the K-means algorithm has a great influence on the experimental results is improved.In this paper,the UCI data set is used to simulate the proposed algorithm.Compared with the K-means algorithm and the K-means algorithm based on bacterial foraging optimization,the experimental results show that the algorithm is better.(2)In this paper,a competitor recognition model based on improved bacterial foraging optimization clustering was proposed.Firstly,the related concepts of competitor identification are defined,and then the competitor recognition model is proposed based on the K-means algorithm of mixed bacterial foraging optimization.(3)Using the competitor identification model proposed in this paper to conduct empirical research on the publicly available collection data of users in the automotive field,and briefly analyze each type of competitive sub-market to build a competitive network map in the automotive field.In general,the research results of this paper provide enterprises and related scholars with a possible way of research on the issue of competitor identification.