Abstract
Excessive or insufficient business hall resources may result in unreasonable resource allocation, adversely affecting the value of an entity business hall. Therefore, proper characteristic parameters are the key factors for analyzing the business hall, which strongly affect the final analysis results. In this study, a characteristic analysis method for the economic operation of a business hall is developed and the feature engineering is established. Because of its simplicity and versatility, the k -means algorithm has been widely used since it was first proposed around 50 years ago. However, the classical k -means algorithm has poor stability and accuracy. In particular, it is difficult to achieve a suitable balance between of the centroid initialization and the clustering number k . We propose a new initialization (LSH- k -means) algorithm for k -means clustering. This algorithms is mainly based on locality-sensitive hashing (LSH) as an index for computing the initial cluster centroids, and it reduces the range of the clustering number. Furthermore, an empirical study is conducted. According to the load intensity and time change of the business hall, an index system reflecting the optimization analysis of the business hall is established, and the LSH- k -means algorithm is used to analyze the economic operation of the business hall. The results of the empirical study show that the LSH- k -means that the clustering method outperforms the direct prediction method, provides expected analysis results as well as decision optimization recommendations for the business hall, and serves as a basis for the optimal layout of the business hall.
Highlights
An entity business hall is where a company directly conducts specific business activities, such as commodity trade, business handling, and service
Excessive or insufficient business hall resources may result in unreasonable resource allocation, which adversely affects the value of an entity business hall. erefore, proper characteristic parameters are the key factors for analyzing the business hall, which strongly affect the final analysis results
According to the time change and load trend, multiple variables such as average load rate, actual load trend, and high-frequency load are extracted as the characteristic indexes of the business hall
Summary
An entity business hall is where a company directly conducts specific business activities, such as commodity trade, business handling, and service. Ey determined the optimal number of service windows by acquiring and presenting a large amount of data. Scientific Programming to establish the channel analysis model for an electricity business hall and optimized the resource allocation. The different settings of the parameters and random selection of the initial clustering centers make the classical k-means algorithm unstable. Erisoglu et al [20] proposed an incremental approach for computing the initial clustering centers. Erefore, we establish an index system for analyzing the efficiency of a business hall. We implement the relevant algorithms and present the optimal allocation scheme for the business hall. (2) By combining the characteristics of k-means and LSH, We propose a new initialization (LSHk-means) algorithm for k-means clustering. Us, LSH-k-means can be efficiently used for the operational analysis of a business hall.
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