Weighted Mining Frequent Itemsets Using FP-Tree Based on RFM for Personalized u-Commerce Recommendation System

Cho, Young Sung; Moon, Song Chul

doi:10.1007/978-3-642-40675-1_66

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…Therefore, it has a certain practical significance in that it effectively carries out customer management. At present, there have been some customer value reviews of the price system, but the measurement model is not mature enough [40]. The first measurement index is the direct profit contribution of customers to the enterprise.…”

Section: Stage 2: Network Executionmentioning

confidence: 99%

Research and Application of Improved Clustering Algorithm in Retail Customer Classification

Chu

Liu

2021

Symmetry

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Clustering is a major field in data mining, which is also an important method of data partition or grouping. Clustering has now been applied in various ways to commerce, market analysis, biology, web classification, and so on. Clustering algorithms include the partitioning method, hierarchical clustering as well as density-based, grid-based, model-based, and fuzzy clustering. The K-means algorithm is one of the essential clustering algorithms. It is a kind of clustering algorithm based on the partitioning method. This study’s aim was to improve the algorithm based on research, while with regard to its application, the aim was to use the algorithm for customer segmentation. Customer segmentation is an essential element in the enterprise’s utilization of CRM. The first part of the paper presents an elaboration of the object of study, its background as well as the goal this article would like to achieve; it also discusses the research the mentality and the overall content. The second part mainly introduces the basic knowledge on clustering and methods for clustering analysis based on the assessment of different algorithms, while identifying its advantages and disadvantages through the comparison of those algorithms. The third part introduces the application of the algorithm, as the study applies clustering technology to customer segmentation. First, the customer value system is built through AHP; customer value is then quantified, and customers are divided into different classifications using clustering technology. The efficient CRM can thus be used according to the different customer classifications. Currently, there are some systems used to evaluate customer value, but none of them can be put into practice efficiently. In order to solve this problem, the concept of continuous symmetry is introduced. It is very important to detect the continuous symmetry of a given problem. It allows for the detection of an observable state whose components are nonlinear functions of the original unobservable state. Thus, we built an evaluating system for customer value, which is in line with the development of the enterprise, using the method of data mining, based on the practical situation of the enterprise and through a series of practical evaluating indexes for customer value. The evaluating system can be used to quantify customer value, to segment the customers, and to build a decision-supporting system for customer value management. The fourth part presents the cure, mainly an analysis of the typical k-means algorithm; this paper proposes two algorithms to improve the k-means algorithm. Improved algorithm A can get the K automatically and can ensure the achievement of the global optimum value to some degree. Improved Algorithm B, which combines the sample technology and the arrangement agglomeration algorithm, is much more efficient than the k-means algorithm. In conclusion, the main findings of the study and further research directions are presented.

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Section: Stage 2: Network Executionmentioning

confidence: 99%

Research and Application of Improved Clustering Algorithm in Retail Customer Classification

Chu

Liu

2021

Symmetry

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“…Automatic text classification technology can achieve effective classification and extraction of text data; at the same time, it can improve the utilization rate of text data and precision of retrieval, and so on. Data volume is growing exponentially [3][4][5], thus, automatic text classification technology is particularly important in data mining and web mining research fields.…”

Section: Open Accessmentioning

confidence: 99%

SAW Classification Algorithm for Chinese Text Classification

et al. 2015

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Abstract:Considering the explosive growth of data, the increased amount of text data's effect on the performance of text categorization forward the need for higher requirements, such that the existing classification method cannot be satisfied. Based on the study of existing text classification technology and semantics, this paper puts forward a kind of Chinese text classification oriented SAW (Structural Auxiliary Word) algorithm. The algorithm uses the special space effect of Chinese text where words have an implied correlation between text information mining and text categorization for high-correlation matching. Experiments show that SAW classification algorithm on the premise of ensuring precision in classification, significantly improve the classification precision and recall, obviously improving the performance of information retrieval, and providing an effective means of data use in the era of big data information extraction.

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“…As a result, systems that recommend content and products that are likely to be preferred by users are widely employed, and various personalized services utilizing such recommendation systems have been provided [18,19,20]. Therefore, studies on recommendation schemes considering users' preferences have been conducted actively to take various user requirements into account quickly [9,10,18,21,22].…”

Section: Introductionmentioning

confidence: 99%

Social group recommendation based on dynamic profiles and collaborative filtering

Bok

Lim

Yang³

et al. 2016

Neurocomputing

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Weighted Mining Frequent Itemsets Using FP-Tree Based on RFM for Personalized u-Commerce Recommendation System

Cited by 11 publications

References 9 publications

Research and Application of Improved Clustering Algorithm in Retail Customer Classification

Research and Application of Improved Clustering Algorithm in Retail Customer Classification

SAW Classification Algorithm for Chinese Text Classification

Social group recommendation based on dynamic profiles and collaborative filtering

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