Fast clustering algorithm of commodity association big data sparse network

Modified FDP cluster algorithm and its application in protein conformation clustering analysis

Customer relationship management (CRM) at supermarkets is willing to interact with customers appropriately with the aim of making strong relationship and resultantly gaining maximum profits. Customers consist of various groups of people and have different needs, styles and expectations. Marketing management of a supermarket segments customers to respond their different demands correctly. Another important concern of supermarket managers is to detect profitable customers. These customers supply main profit of company and saving them guarantees existence of the supermarket. This research presents a model completing CRM process from understanding customers to assigning marketing strategies. Profitable customers will be distinced as a result of correct understanding of all customers. Present research is comprised of two phases. At phase one, dataset with recency, frequency and monetary (RFM) measures is constructed and clustered using K-means algorithm. Six segments of customers are detected based on the results of clustering. All segments are comprehensively analyzed and marketing strategies for them are described in phase two. Transactions of every segment of customers are separated and association rules are extracted using market basket analysis and Apriori algorithm. Consequent and also antecedent product items are proposed to customers who purchased antecedent product iems. So, dedicated marketing proposals are developed for some special customers.

Association rules are widely used to extract patterns from a given database. The association rules are capable of finding correlations among items, making it possible for the user to learn which items are present in the transactions and which of them have a significant correlation. One of the major problems with association rules is that the number of extracted rules usually exceeds the number of transactions present in the database, also surpassing the user’s capability to explore the obtained knowledge. To overcome this problem, the post-processing phase was proposed with the objective of directing the user to the rules that potentially have the most interesting knowledge. One of the used approaches is to divide the association rules into groups (or clusters), so that rules behave similarly are on the same group, facilitating the rule set understanding. In the literature, there are some works that uses clustering algorithms to split the rules while some other works use community detection algorithms. As both approaches obtain groups of association rules, but using different premises, different results can be obtained. No study has been done on the differences among clustering and community detection algorithms, which makes the selection of the algorithm hard, once their behavior is not well known in the association rule post-processing phase. This paper presents an analysis on both approaches, aiming to find the differences and the similarities among them, making it easier to select an approach by knowing its behavior.

The k-means clustering algorithm is a very classical clustering algorithm that is widely used because of its excellent efficiency and performance. The algorithm uses Euclidean distance to calculate the similarity between samples and iteratively updates the membership matrix to obtain clustering results. However, when k-means algorithm clusters datasets containing samples with intra-cluster distances greater than inter-cluster distances, errors often occur when partitioning the boundary samples, which eventually leads to unsatisfactory results. Moreover, although k-means algorithm makes the intra-cluster distance as small as possible, it neglects to maximize the inter-cluster distance, and eventually only finds the local optimal solution. Different from the existing k-means type algorithm, this paper proposes a similarity measure based on the impact factor, which determines the partitioning result by comparing the impact of samples on each cluster. And on the basis of the objective function of k-means algorithm, we combine the inter-cluster distance to solve the defects of local optimality that exist in k-means algorithm. In the paper, we theoretically analyze and prove the proposed method, and compare and analyze the clustering results of the algorithm with the class k-means algorithm on real datasets, and confirm that the proposed algorithm in this paper can effectively avoid the defects of the class k-means algorithm.

With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big limitations that the K-Means algorithm has, namely requirements that users give the anticipated variety beforehand integer K and random selection of initial variety center, proposed K-Means improved algorithm based on the association rules technology. This method proposed the concept of the smallest rule covering set .It has relieved these two big limitations of K-Means algorithm effectively. It is used for the audit monitor target discovery and extraction process in social security domain basic old-age insurance audit methods. Thus it can provide better reference value and guiding sense for auditors.

Recently, a variety of data acquisition methods leads to the description of the same thing from different angles, which induces a large number of multi-view data. Clustering multi-view data in large-scale data analysis is of great significance and very challenging. In this paper, we present a new robust clustering algorithm, the Multi-view Fuzzy K-means clustering (MFKC) algorithm. In MFKC, a fuzzy membership method is used and optimized to describe the probability of a sample belonging to certain clusters. Compared with the hard partition based multi-view clustering algorithms, MFKC can effectively describe the fuzzy relationship between multi-view data and all clusters, and has better interpretability for the clustering results. In addition, MFKC also sets different weights for different views. By optimizing the multi-view clustering objective function, we obtain the optimal weigh of each view to reflect the importance of the view. The feasibility and effectiveness of the proposed algorithm is validated through various experimental comparisons.

Clustering is a process of classifying data into different classes and has become an important tool in data mining. Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simplicity and high efficiency. However, the traditional K-means algorithm can only find spherical clusters, and is also susceptible to noise points and isolated points, which makes the clustering results affected. To solve these problems, this paper proposes an improved K-means algorithm based on kurtosis test. The improved algorithm can improve the adaptability of clustering algorithm to complex shape datasets while reducing the impact of outlier data on clustering results, so that the algorithm results can be more accurate. The method used in our study is known as kurtosis test and Monte Carlo method. We validate our theoretical results in experiments on a variety of datasets. The experimental results show that the proposed algorithm has larger external indicators of clustering performance metrics, which means that the accuracy of clustering results is significantly improved.

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.

Clustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm to select the initial centers is proposed. In the proposed algorithm, dispersion degree is defined, which is based on entropy. In the algorithm, all the objects are firstly grouped into a big cluster, and the object that has the maximum dispersion degree and the object that has the minimum dispersion degree are selected as the initial clustering centers from the initial big cluster. And then other objects in the biggest cluster are partitioned to the initial clusters to which the objects are nearest. The partition process will be repeated until the cluster number is equal to the specified value k. Finally, the partitioned k clusters and their cluster centers are applied to k-means algorithm as initial clusters and centers. Several experiments are conducted on real data sets to evaluate the proposed algorithm. The proposed algorithm is compared with traditional k-means algorithm and max-min distance clustering algorithm, and experimental results show that the improved k-means algorithm is stable in selecting initial clustering, because it can select unique initial clustering centers. The optimized algorithm’s effectiveness and feasibility are also verified by experiments, and the algorithm can reduce the times of iterations and has more stable clustering results and higher accuracy.

A field experiment on rice was conducted at RRS, Kaul (Kaithal) of Chaudhary Charan Singh Haryana Agricultural University, Hisar during Kharif season of 2022 to find out optimum dose of nitrogen in rice under late planted condition for getting higher yield. The transplantation get delayed because of shortage of labour and water during the brief transplanting period, which may affect the crop yield adversely under late planting. Soil of experimental field was sandy clay loam texture, alkaline in reaction (7.81), low in organic carbon (0.39%), available nitrogen (130 kg ha-1) and medium in available phosphorous (24 kg ha-1) and high in available potash (480 kg ha-1). The experiment included two transplanting dates (P1: First week of July and P2: Fourth week of July), five levels of N application (N1:0, N2:30, N3:60, N4:90, N5:120 kg N ha-1) laid out in factorial randomised block design. The growth parameters viz. plant height (cm), number of tillers m-2 and dry matter accumulation (g) were reduced significantly under late planting (P2). The yield (grain and straw) of the rice crop increased with each successive increase in N level, although the response was only significant up to 90 kg ha-1. The interaction between planting time and N levels was found to be significant in terms of grain yield, revealing that dose of 90 kg ha-1 was sufficient in timely transplanted rice crops (1st week of July), whereas late transplanted crops (4th week of July) may require a higher dose of N (120 kg ha-1) to get higher yield. However, the highest yield was obtained when the crop was timely transplanted (1st week of July) and supplied 90 kg of N ha-1.

Air quality is one of the most popular environmental problems in this globalization era. Air pollution is the poisonous air that comes from car emissions, smog, open burning, chemicals from factories and other particles and gases. This harmful air can give adverse effects to human health and the environment. In order to provide information which areas are better for the residents in Malaysia, cluster analysis is used to determine the areas that can be clustering together based on their a ir quality through several air quality substances. Monthly data from 37 monitoring stations in Peninsular Malaysia from the year 2013 to 2015 were used in this study. K - Means (KM) clustering algorithm, Expectation Maximization (EM) clustering algorithm and Density Based (DB) clustering algorithm have been chosen as the techniques to analyze the cluster analysis by utilizing the Waikato Environment for Knowledge Analysis (WEKA) tools. Results show that K - means clustering algorithm is the best method among ot her algorithms due to its simplicity and time taken to build the model. The output of K - means clustering algorithm shows that it can cluster the area into two clusters, namely as cluster 0 and cluster 1. Clusters 0 consist of 16 monitoring stations and clu ster 1 consists of 36 monitoring stations in Peninsular Malaysia.

Forest fire is one of the most dangerous natural hazards of planet earth now. Here presenting an approach where it has been trying to analyze the danger of fire for the forests of Brazil. The dataset contains data from 1998 to 2017 for all the states of Brazil where a forest fire has been caught throughout the year. An unsupervised approach like—K-Means clustering, Fuzzy C-Means, and Apriori algorithm was used to do so. This is a large dataset containing 6454 unlabeled data, to build a model with them K-Means clustering seems helpful. It tries to build subgroups (clusters) of similar data points from a large group. Fuzzy C-Means is also an unsupervised algorithm and it’s working process is similar to K-Means clustering. By using K-Means clustering, Fuzzy C-Means, and Apriori method the regions which are in risk of fire danger were detected.

The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clusters or micro-clusters, then merges those conjoint micro-clusters by using a merging judgement process. For some datasets that ESynC and SynC cannot detect correct clusters, CESynC can capture natural clusters. From the simulation experiments, we observe that CESynC can get better (or the same) clustering results than (or as) that of ESynC in many cases. We also observe that the clustering results of CESynC and ESynC are often better than that of SynC. Therefore, we can say CESynC can often obtain better clustering quality than ESynC and SynC in some kinds of datasets. Further comparison experiments with some classical clustering algorithms demonstrate the clustering effect of CESynC.

Clustering ensemble technique has been shown to be effective in improving the accuracy and stability of single clustering algorithms. With the development of information technology, the amount of data, such as image, text and video, has increased rapidly. Efficiently clustering these large-scale datasets is a challenge. Clustering ensembles usually transform clustering results to a co-association matrix, and then to a graph-partition problem. These methods may suffer from information loss when computing the similarity among samples or base clusterings. Rich information between samples and base clusterings is ignored. Moreover, the results are not discrete. They need post-processing steps to obtain the final clustering result, which will deviate greatly from the real clustering result. To address this problem, we propose a co-clustering ensemble based on bilateral k-means (CEBKM) algorithm. Our algorithm can simultaneously cluster samples and base clusterings of a dataset, to fully exploit the potential information between the samples and the base clusterings. In addition, it can directly obtain the final clustering results without using other clustering algorithms. The proposed method, outperformed several state-of-the-art clustering ensemble methods in experiments conducted on real-world and toy datasets.

We applied data mining techniques to analyze customer behavior for an online Saudi store using historical purchase data. Unsupervised learning was used to segment customer behaviors using the following clustering algorithms: k-means clustering, hierarchical clustering,and DBSCAN. Clustering results were compared on the basis of the Silhouette score, Davies-Bouldin index, and Calinsk-Harabasz index. The results revealed that k-means clustering with $\mathrm{k}=5$ produced the most logical clusters. The clustering techniques managed to segregate customers into different segments according to total payments and the number of purchases they made. The clusters showed slight differences in customer spending habits. This approach had potential merit for the online store, as it makes it easy to target marketing for each category, such as presenting discount coupons, introducing new products and services, and making changes to existing services according to customer requirements.