Objective: Utilize kmeans clustering to segment customers of a mall based on their spending behavior, aiming to provide personalized services and improve marketing strategies. Dataset: Use the "Mall Customer Segmentation Data" available on the UCI Machine ...
A Sparse K-Means Clustering Algorithm Name: *** ID: *** K-means is a broadly used clustering method which aims to partition observations into clusters, in which each observation belongs to the cluster with the nearest mean. The popularity of K-means derives in part from its conceptual simpl...
It confirms that balanced clustering is effective for learning representations. If de- creasing the ratio, each cluster can have a different number of instances that is more flexible to capture the inherent data structure. For example, when γ′ = 0.8, the ...
The multiple kernel learning technique and K-means clustering which is used to forecast stock price changes and incorporate information from the target company and its homogenous cluster. The experiment was conducted utilizing three years of data from the Republic of Korea. The results reveal that ...
For example, the work in [11] proposes a multiple kernel k-means clustering algorithm with a matrix-induced regularization term to re- duce the redundancy of the selected kernels. A local kernel alignment variant is then developed by sufficiently consid- ering the variation among sample, which ...
We chose clustering technique over any other supervised technique such as classification, since crimes vary in nature widely and crime database are often filled with unsolved crimes. Therefore, classification technique that will rely on the existing and known solved crimes, will not give good ...
一种融合SOM与K_means算法的动态信用评价方法及应用_张发明
一种融合SOM与K means算法的动态信用评价方法及应用张发明(南昌大学经济与管理学院,江西南昌330031)摘要:针对传统信用评价方法多是静态评价的不足,本文提出了一种融合SOM与K means算法的动态信用评价方法。文章首先对动态信用评价问题进行了介绍,并利用E TOPSIS方法对单时点下的静态信息进行集结,以确定被评价对象的信用...
The example indicates that both disturbing factors have to be considered when selecting an optimal k for k-means. In the proposed k-means-based clustering approach, we directly downweight the effect of outliers by observation weights \(v_i\). However, the impact of noise variables, which is...
Clustering is a very popular machine-learning technique that is often used in data exploration of continuous variables. In general, there are two problems commonly encountered in clustering: (1) the selection of the optimal number of clusters, and (2) th