Commodity correlation big data sparse network quick clustering method

Abstract

The invention discloses a commodity correlation big data sparse network quick clustering method which comprises the steps of performing preprocessing on commodity sale recording data, establishing and storing a commodity correlation big data sparse network; performing pruning on the commodity correlation big data sparse network; and performing quick clustering on the commodity correlation big data sparse network, thereby obtaining a clustering result. According to the method of the invention, a single-step linked list structure is utilized for storing a relation between commodity nodes, and only relations of the commodity nodes which are directly connected with the commodity nodes are marked, and a redundant space in a common purchasing relation matrix is eliminated. According to the method of the invention, pruning is performed on low-degree commodity nodes for improving digging efficiency and improving clustering precision. According to the commodity correlation big data sparse network quick clustering method, a concept of dividing high-degree commodity nodes by low-degree commodity nodes can eliminate restriction by a traditional clustering algorithm, and judgment is directly performed according to the value of the degree. A clustering process can be finished without consideration for data distribution, no multiple times of data accessing or prior knowledge.

Description

technical field [0001] The invention belongs to big data processing technology, in particular to a sparse network processing method for commodity-associated big data. Background technique [0002] With the comprehensive application of information systems and information construction, retail enterprises generate a large number of data records in operation management, and mining the business perspective and market rules among these data has considerable application value. By studying the shopping transaction data of retail enterprises and mining the correlations and frequent item sets among commodities, retail enterprises can be guided to make more scientific and reasonable operational decisions such as category management, store layout display, and product promotion, and greatly improve the enterprise's profitability. Operating results. With the rapid development of e-commerce, Amazon, JD.com, Yihaodian and other e-commerce retail enterprises provide marketing services such ...

AI Technical Summary

Problems solved by technology

The currently widely used adjacency matrix and adjacency linked list storage methods are used to analyze and mine potential laws and require multiple access to data, so they are not suitable for big data association analysis of retail products.

However, the clustering of large data sparse network oriented to retail goods needs to focus on improving efficiency. Commonly used complex network clustering needs to give an effective objective function and optimal solution search strategy in advance, and the search process sacrifices efficiency to improve the accuracy of clustering. , which is not suitable for clustering retail commodity association big data sparse networks

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