Hierarchical Clustering Algorithm Based on Fast and Uniform Segmentation

Clustering is one of the techniques used to obtain useful information from web log file for better understanding of customer behaviour. Two clustering techniques that commonly used are Greedy Hierarchical Item Set-Based Clustering (GHIC) algorithm and Hierarchical Clustering Algorithm (HCA). The algorithms, however, have its weaknesses in terms of processing times and time complexity. This paper proposes a new approach called Hierarchical Pattern-Based Clustering (HPBC) algorithm to improve the processing times based on the difference of mean support values of each cluster. The simulation revealed that the proposed algorithm outperformed the HCA and GHIC up to 100% and 50% respectively, with less time complexity.

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from huge database. Data mining (DM) technology has emerged as a means of performing this discovery. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Many data mining methods and algorithms have been developed and applied in a lot of application fields [1]. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining [2]. In this talk, some basic issues of data mining are addressed. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? Through analyzing existing data mining methods, and domain-driven (or user-driven) data mining models [3-5], we find that we should take a data mining process as a process of knowledge transformation. Based on this understanding of data mining, a conceptual data mining model of domain-oriented data-driven data mining (3DM) is proposed [2]. The relationship between traditional domain-driven (or user-driven) data mining models and our proposed 3DM model is also analyzed. Some domain-oriented data-driven data mining algorithms for mining such knowledge as default rule [6], decision tree [7], and concept lattice [8] from database are proposed. The experiment results for these algorithms are also shown to illustrate the efficiency and performance of the knowledge acquired by our 3DM data mining algorithms.

Clustering is an effective unsupervised machine learning method that can be used as a stand-alone heuristic or as a part of a data mining process. The goal of clustering analysis is to partition data into groups with high intra-cluster association, and low inter-cluster association. Hierarchical clustering requires minimal parameters, has flexibility with similarity measure, and has strong visualization power, all of which makes it ideal for exploratory analysis. Hierarchical clustering is a particular branch of clustering algorithms where the results are not given as partitions, but rather a nested structure, which can represent the ordering among elements within a dataset. The study of the performance of hierarchical structure on time series data is limited. In this paper, we examine the hierarchical structure of time series datasets. The most popular hierarchical structured clustering heuristics include the Hierarchical Agglomerative Clustering, which is distance based; and Ordering Points To Identify the Clustering Structure, which is density based. Both share many similar characteristics and are suitable for time series data processing. We examine the performance of different hierarchical clustering algorithms with time series both internally as well as externally.

In this paper, new parallel algorithms for single and complete link hierarchical agglomerative clustering are presented. The parallel algorithms have been mapped on a SIMD machine model with a linear interconnection network. The model consists of a linear array of N PE's, where N is the number of patterns, interfaced with a global host machine and the interconnection network provides inter-PE and PE-to-host/host-to-PE communication. The proposed algorithms are faster than previously known algorithms for hierarchical clustering. For clustering a data set with N patterns, using N PE's, the computation time for the single link clustering algorithm is shown to be O(NlogN) and that for the complete link clustering algorithm is shown to be O(N/sup 2/). The parallel algorithms have been verified through simulations on the Intel's iPSC/2 concurrent supercomputer.

The two most widely studied clustering algorithms are partitional and hierarchical clustering. These algorithms have been heavily used in a wide range of applications primarily due to their simplicity and ease of implementation relative to other clustering algorithms. Partitional methods need to be provided with a set of initial seeds which are then improved iteratively. Hierarchical clustering can be achieved in two different ways, namely, bottom-up and top-down clustering. Hierarchical methods can start off with the individual data points in single clusters and build the clustering. Hierarchical clustering algorithms approach the problem of clustering by developing a binary tree-based data structure called the dendrogram. Once the dendrogram is constructed, one can automatically choose the right number of clusters by splitting the tree at different levels to obtain different clustering solutions for the same dataset without rerunning the clustering algorithm again.

We introduce a new intrusion detection method based on the Hierarchical Clustering Algorithm (HCA), to detect anomalous user’s profiles. In the Unix system, a simple user has only some privileges (can access to some resources), but the root user has more privileges. So, we can speak here about hierarchy of users. By the same way, we can use a hierarchy of users in intrusion detection field, to distinguish between the normal user and suspicious user. Many data mining methods were already used in previous works in intrusion detection. Even if some of them led to interesting results, but they still suffer from some weaknesses. This is the reason why we focused in this study on the use of the HCA to detect anomalous profiles. A survey of intrusion detection methods is presented. The HCA procedure is described in detail. Our simulation results demonstrate the robustness of our approach in comparison to some previous used methods.

MGR: An information theory based hierarchical divisive clustering algorithm for categorical data

This work presents a study about dissimilarity measures for seismic signals, and their relation to clustering in the particular problem of the identification of earthquake focal mechanisms, i.e. the physical phenomena which have generated an earthquake. Starting from the assumption that waveform similarity implies similarity in the focal parameters, important details about them can be determined by studying waveforms related to the wave field produced by earthquakes and recorded by a seismic network. Focal mechanisms identification is currently investigated by clustering of seismic events, using mainly cross-correlation dissimilarity in conjunction with hierarchical clustering algorithm. By the way, it results that such adoptions have not been sufficiently validated. To shed light on this we have studied the cross correlation dissimilarity on simulated seismic signals in conjunction with hierarchical and partitional clustering algorithms, and compared its performance with a newly one recently introduced for the purpose called cumulative shape. In particular, we have properly created synthetic waveforms related to two types of focal mechanisms, showing that the cumulative shape perform better than cross-correlation in the identification of the expected clustering solution.

Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms.

Explainable semi-supervised AI for green performance evaluation of airport buildings

Aiming at the problem about initial clustering center was randomly assigned in K-means clustering algorithm, the improved K-means clustering algorithm based on hierarchical clustering algorithm and K-means clustering algorithm was proposed in this paper. In the improved algorithm, first of all K was calculated by hierarchical clustering. When K was determined, K-means clustering was implemented. The results of the aero-engine vibration data clustering shown that not only the k value was to quickly and accurately determined, but also the number of clusters can be reduced and higher computing efficiency can be attained by the improved K-means clustering algorithm.

The proliferation of big data and the growth of e-commerce have intensified the challenges associated with extracting actionable data for personalised recommendations and decision-making. With data-driven marketing strategies, understanding and predicting customer behaviour has become paramount for maintaining competitive advantage. This study leverages business analytics tools, focusing on Recency, Frequency, and Monetary (RFM) Analysis, alongside K-Means and Hierarchical (Agglomerative) Clustering algorithms, to segment customer transactional data. Data normalisation, a critical step for accurate clustering, was performed using log transformation and the Power Transformer technique with the Yeo-Johnson parameter, the latter proving more effective for handling both positively and negatively skewed data, enhancing data normalisation and suitability for analysis. This study reveals that RFM Analysis with Hierarchical Clustering outperforms K-Means Clustering, achieving a Silhouette Score of 0.47 and a Calinski–Harabasz Index of 3787.1, indicating a more accurate identification of customer segments. RFM Analysis alone generated eight clusters, while integrating RFM Analysis with both Hierarchical Clustering and K-Means generated three similar-sized clusters with interchanged labels. These metrics highlight the proficiency of Hierarchical Clustering in identifying unique customer segments and customising marketing strategies. The findings indicate that the RFM-Hierarchical Clustering approach enhances segmentation precision and facilitates more refined and effective marketing strategies.

Clustering has been used in literature to enhance classification accuracy. But most partitional clustering methods need the number of clusters as input and also they are sensitive to initialization. Although hierarchical clustering methods may be more effective in finding clustering structure of the dataset than partitional methods but hierarchical clustering methods give tree structure known as dendrogram which is a sequence of clustering solutions. Hence hierarchical clustering algorithms are not generally applied in the preprocessing step to classification methods. This problem can be solved by cutting the dendrogram to get single clustering solution. In this paper we propose a framework for classification which uses Optimal Clustering Genetic Algorithm (OCGA) to obtain optimal level of cutting the dendrogram. A single clustering solution is obtained by cutting the dendrogram at optimal level. The clusters obtained are used to enhance classification accuracy of the classification methods. The proposed classification methods have been applied for the diagnosis of diabetes disease.

PurposeIn accounting and finance, researchers have used many ways to detect manager’s fraud risk. Until now, many researchers have used some data mining methods in these two fields to detect this risk. The purpose of this paper is to compare the precision of two data mining methods in detecting such a risk.Design/methodology/approachFor this purpose, this paper analyzed the texts of board’s reports and used two methods including the convex optimization (CVX) method and least absolute shrinkage and selection operator (LASSO) regression method. In this way, the words of these reports, which have the greatest power in explaining the manager’s high fraud risk index, were identified. Using these words, this paper presented a model that could detect manager’s high fraud risk index in companies.FindingsThe results indicated that both methods can detect the manager’s high fraud risk index with a precision between 82.55 and 91.25 percent. The LASSO method was significantly more precise than the CVX method.Research limitations/implicationsThe lack of access to an official and reliable list of firms suspected to fraud and the lack of access to the Microsoft Word (MS Word) file of board’s reports were two of the most important limitations of this study.Practical implicationsRegulatory bodies and independent auditors can consider the proposed methods in this study for assessing the fraud risk for a firm or other legal parties.Originality/valueThis paper avoided using merely financial statements data to detect the manager’s fraud risk index and focused on texts of board’s reports for the detection process. The capabilities of data mining and text mining methods for detecting the manager’s fraud risk index using board’s reports were tested in this paper. By comparing CVX and LASSO results, this paper indicated that methods with a binary-dependent variable have more power and are more precise than methods with continuous-dependent variables for detecting fraud.

A novel similarity measure based on spatial overlapping relation is proposed in this paper, which calculates the similarity between a pair of data points by using the mutual overlapping relation between them in a multi-dimensional space. A spatial overlapping based hierarchical clustering method SOHC was also developed and implemented aimed to justify the effectiveness of the proposed similarity measure. SOHC works well both in low-dimensional and high-dimensional datasets, and is able to cluster arbitrary shape of clusters. Moreover, it can work for both numerical and categorical attributes in a uniform way. Experimental results carried out on some public datasets collected from the UCI machine learning repository and predictive toxicology domain show that SOHC is a promising clustering method in data mining.