Stock is one of the investment assets that has its charm for investors. It is very liquid and has a high rate of return, but it has a high risk. The strategy commonly used to minimize investment risk is to diversify through portfolio formation. A good allocation of funds must be determined in forming an optimal portfolio. In addition, the method of stock selection needs to be considered so the stocks are well diversified and the portfolio developed has good performance. This study aims to compare stock selection between K-Means and Average Linkage clustering approaches in forming an investment portfolio. Clustering analysis is used to group IDX80 stocks based on their attributes. In forming a portfolio with the Mean-VaR model, the stock selection decision criteria used are by selecting stocks with the highest positive returns from each cluster. As a result, the two clustering techniques show the superiority of the Silhouette score for a certain number of clusters, but there are still more advantages in Average Linkage. The portfolio approached by Average Linkage resulted in a better performance than the portfolio approached by K-Means. Therefore, Average Linkage clustering can be used as a better recommendation in decision-making to select stocks so as to produce optimal portfolio performance.

Micro, Small, and Medium Enterprises (MSMEs) are a vital component of local economic growth, making significant contributions to advancing Smart Village and Smart Economy programs. The classification and distribution processes to support MSMEs across numerous locations continue to be manual, subjective, and inefficient. This study employs a hybrid data mining methodology that combines the K-Means clustering algorithm with Random Forest classification to enhance the precision and impartiality of MSMEs categorization in Bogor Regency. The study adheres to the Knowledge Discovery in Databases (KDD) framework, which includes data selection, cleansing, transformation, mining, and evaluation. The datasets consist of 7,615 MSMEs obtained in early 2025 from the Bogor Regency Cooperative and MSMEs Office. Following preprocessing and feature selection, K-Means identified an optimal cluster count of k = 3 using the Elbow method. The clusters were used as training labels for the Random Forest model, which achieved 95.9% accuracy, 0.94 precision, and 0.94 recall. Cluster validation produced a Silhouette Score of 0.6926 and a Davies–Bouldin Index of 0.994, signifying robust separation and cohesion. The results indicate that the K-Means–Random Forest hybrid model markedly enhances classification efficacy and establishes a data-driven basis for empowering MSMEs with Smart Village and Smart Economy paradigms.

Text Clustering consists of grouping objects of similar categories. The initial centroids influence operation of the system with the potential to become trapped in local optima. The second issue pertains to the impact of a huge number of features on the determination of optimal initial centroids. The problem of dimensionality may be reduced by feature selection. Therefore, Wind Driven Optimization (WDO) was employed as Feature Selection to reduce the unimportant words from the text. In addition, the current study has integrated a novel clustering optimization technique called the WDO (Wasp Swarm Optimization) to effectively determine the most suitable initial centroids. The result showed the new meta-heuristic which is WDO was employed as the multi-objective first time as unsupervised Feature Selection (WDOFS) and the second time as a Clustering algorithm (WDOC). For example, the WDOC outperformed Harmony Search and Particle Swarm in terms of F-measurement by 93.3%; in contrast, text clustering's performance improves 0.9% because of using suggested clustering on the proposed feature selection. With WDOFS more than 50 percent of features have been removed from the other examination of features. The best result got the multi-objectives with F-measurement 98.3%.

Clustering is a popular data mining technique for grouping a set of objects into clusters so that objects in one cluster are very similar and objects in different clusters are quite distinct. K-means (KM) algorithm is an efficient data clustering method as it is simple in nature and has linear time complexity. However, it has possibilities of convergence to local minima in addition to dependence on initial cluster centers. Artificial Bee Colony (ABC) algorithm is a stochastic optimization method inspired by intelligent foraging behavior of honey bees. In order to make use of merits of both algorithms, a hybrid algorithm (MABCKM) based on modified ABC and KM algorithm is proposed in this paper. The solutions produced by modified ABC are treated as initial solutions for the KM algorithm. The performance of the proposed algorithm is compared with the ABC and KM algorithms on various data sets from the UCI repository. The experimental results prove the superiority of the MABCKM algorithm for data clustering applications.

Research has been done on evaluating the performance more emphasis either on the technical aspects of the performance evaluation or considers only one aspect of banks such as sales, revenue, etc. This paper presents an empirical investigation to measure the relative efficiency of different banks using a hybrid of K-means and Grey relational analysis. The study uses eight criteria such as resources, customer satisfaction, etc. for clustering different banks and using Grey relational analysis provides ranking of different branches of a bank named Bank Qarzol Hasane Resalat located in city of Tehran, Iran. The preliminary study has indicated that the proposed model of this paper may capable of providing some promising results.

In today' s extremely competitive environment, customers are the most important asset of any business and success of any organization depends on loyal customer retention. Therefore, it is important to learn more about different groups of customers and propose appropriate plans to take care of them. Customer segmentation is one of the most common methods to analyze customer’s behavior. The market can be divided into several smaller homogeneous groups, which helps organizations deliver targeted marketing techniques and provides optimized techniques for resource allocation. This paper uses the information gathered from Point of Sales (POS) in one of Iranian private banks and using two methods of K-Means and Kohonen, customers are clustered into four segments to detect the most profitable customers.

Performance measurement plays essential role on improving the performance of business units and their efficiencies. During the past few years, there have been tremendous development in banking systems and the primary focus of many managers is to improve the quality of services for market retention. Performance measurement in banking industry is normally involved with various qualitative as well as quantitative criteria, which leads to the implementation of multiple criteria decision making techniques. This paper presents a hybrid grey relational analysis and K-means to cluster and measure the performance of banking system. The proposed study uses different criteria, clusters banks into various segments and ranks 43 different banks in city of Semnan, Iran.