A Spatial Overlapping Based Similarity Measure Applied to Hierarchical Clustering

A novel travel-time based similarity measure for hierarchical clustering

Clustering algorithms play a pivotal role in data mining, offering powerful tools for uncovering hidden patterns and structures within datasets. These algorithms aim to divide data points into coherent groups based on similarities or dissimilarities, making it easier to explore and understand complex data. Clustering algorithms typically rely on similarity measures to assess the likeness between data points. Consequently, selecting a suitable similarity measure is crucial for achieving satisfactory clustering outcomes. However, this decision can pose significant challenges, especially for non-experts, given the plethora of similarity measures available in the literature and their performance which is closely linked to the specific dataset, clustering algorithm, and cluster validity index employed. This difficulty is even more important when considering mixed data clustering. Mixed data refers to heterogeneous data characterized by both numerical and categorical attributes. In such a context, the same similarity measure cannot be used for both types of attributes due to their different nature. Commonly, two similarity measures are combined, one for numerical attributes and one for categorical attributes. This adds a layer of complexity to the problem since it requires the selection of two similarity measures instead of just one. This paper introduces SIMREC, a similarity measure recommendation system for mixed data clustering. The system uses meta-learning to mine the relationship between dataset characteristics and similarity measures performances for different mixed data clustering algorithms and cluster validity indices. Therefore, given a mixed dataset, a mixed data clustering algorithm, and a cluster validity index, the system can recommend suitable pairs of numerical and categorical similarity measures based on the characteristics of the dataset. We implemented the proposed system using 130 pairs of similarity measures (10 numerical and 13 categorical), 4 commonly used mixed data clustering algorithms (K-Prototypes, LSH-K-Prototypes, K-Medoids, and Hierarchical Clustering), and three cluster validity indices (Silhouette, Clustering Accuracy, and Adjusted Rand Index). Our experiments on 185 publicly available mixed datasets show that the pairs of similarity measures recommended by SIMREC outperform the baseline pairs, including classically used pairs of similarity measures in the literature.

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.

Nowadays many applications generate mixed data objects consisting of numerical and categorical attributes. Simultaneously dealing with mixed objects is more challenging and various approaches convert one type to another one to face this issue. But in many cases this leads to information loss. Therefore integrating categorical and numerical attributes sounds reasonable since it keeps the original format of any attribute. In this paper we focus on clustering and especially density-based clustering as one of the well-known clustering approaches well-performed on arbitrary shape clusters. Density-based clustering algorithms require a distance measure to discover dense regions. Therefore we introduce the distance hierarchy as a distance measure appropriate for both categorical and numerical attributes. However setting the parameters regarding any parametric clustering algorithm could be another issue. Therefore we employ minimum description length principle to automate this process.

Combining hierarchical clustering approaches using the PCA method

The purpose of data clustering algorithm is to form clusters (groups) of data points such that there is high intra-cluster and low inter-cluster similarity. There are different types of clustering methods such as hierarchical, partitioning, grid and density based. Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. A hierarchical clustering method can be thought of as a set of ordinary (flat) clustering methods organized in a tree structure. These methods construct the clusters by recursively partitioning the objects in either a top-down or bottom-up fashion. In this paper we present a new hierarchical clustering algorithm using Euclidean distance. To validate this method we have performed some experiments with low dimensional artificial datasets and high dimensional fMRI dataset. Finally the result of our method is compared to some of existing clustering methods.

Clustering algorithm for mixed datasets using density peaks and Self-Organizing Generative Adversarial Networks

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.

Hierarchical clustering algorithm is a very important method in data mining. The disadvantage of hierarchical clustering lies in the time complexity of the algorithm and the one-way irreversibility of the algorithm. The inaccuracy of the conditions for cluster termination is another major disadvantage of hierarchical clustering. Hierarchical clustering requires the final cluster number. But for most datasets, the number of clusters cannot be known in advance. Therefore, a method is proposed to combine the split-based and agglomeration-based hierarchical clustering algorithms to first quickly and uniformly partition the original dataset, and then make similar partitions adaptively merge based on the partition density and partition distance on the basis of these partitions. In this paper, aiming at these defects of hierarchical clustering, a hierarchical clustering algorithm based on fast and uniform segmentation is proposed.

GCHL: A grid-clustering algorithm for high-dimensional very large spatial data bases

The high use of smartphones at this time led to a decline in public interest in reading books in the library directly. Especially students and students. This is certainly a problem for the Langkat Regency Archives and Libraries Office. Socialization is needed to increase efforts to read interest in the community. The right socialization location must have several criteria so that the socialization carried out is right on target. The existence of a database for each member of the library will facilitate the location selection process. Data mining techniques can classify the number of library members based on the results of large data analysis into information in the form of patterns. The clustering method is a method in data mining that can analyze data with the aim of grouping data based on the same characteristics. The K-Means algorithm is a simple algorithm for classifying a large number of objects with certain attributes into clusters which are usually used in data mining.

The way we learn has changed as a result of advances in science and technology and the growing influence of information technology on daily life. E-learning is a relatively new concept in educational settings, but it has progressively earned a spot in modern training techniques. Data mining methods are used in many applications, particularly in elearning. Comparing direct communications difficult to track the student involvement in online courses. To overcome the above mentioned issues the existing methods are analyzed various techniques of data mining and optimization methods which can be applied to bring out hidden knowledge from the educational data? In the e-learning work, preprocessing, feature selection and classification process are performed to improve the Kalboard 360 educational dataset accuracy significantly. This study focused on educational data mining (EDM) and employed decision trees, support vector machines (SVM), multilayer perceptrons, and the naive bayesian C4.5 algorithm among other classification techniques to assess the features for the prediction of students’ behaviour and academic achievement. This paper analyzes the advantages and shortcomings of each algorithm applied to data set on the basis of accuracy for various classification techniques like Multilayer Perceptron based ANN algorithm (MP-ANN), Decision Tree (DT) C4.5 and Hybrid Regression model with Multi Label Classification (HR-MLC) approach. The experimental result shows that the HR-MLC algorithm provides better performance in terms of higher accuracy, precision, recall and f-measure rather than the other existing methods.

Computing significant factors quantitatively is an imperative task in understanding the underlying reasons that contribute to a final outcome. In this paper, a case of e-Government ranking is studied by attempting to find the significance of each KPIs which leads to resultant rank of a country. Significant factors in this context are inferred as some degrees of relations between the input variables (which are the KPIs in this case) and the final outcome (the rank). In the past, significant factors were either acquired as first-hand information via direct questioning from users’ satisfaction survey or qualitative inference; typical question is ‘You are satisfied with a particular e-Government service’ by applying a multi-level Likert scale. Respondents answered by choosing one of the following: Strongly agree, Agree, Neutral, Disagree, and Strongly Disagree. The replies are then counted and studied using traditional statistical methods. In this paper, an alternative method by feature selection in data mining is proposed which computes quantitatively the relative importance of each KPI with respective to the predicted class, the rank. The main advantage of feature selection by data mining (FSDM) method is that it considers the cross-dependencies of the variables and how they contribute as a whole predictive model to a particular predicted outcome. In contrast, classical significant factor analysis such as correlogram tells only the strength of correlation between an individual pair of factor and outcome. Another advantage of using data mining method over simple statistic is that the inferred predictive model could be used as a predictor and/or what-if decision simulator; given some values of KPIs a corresponding rank could be guesstimated. A case study of computing significant factors in terms of KPIs that lead to the world rank in from the data of UN e-Government Survey 2010, is presented.

The current evaluation model of practical effect of Ideological and political education results in serious distortion due to the poor data processing ability. In view of this problem, the paper constructs the evaluation model of the practical effect of Ideological and political education based on deep data mining. The cluster method in data mining is used to clean and preprocess the practical effect of Ideological and political education. Select the correlation function to construct the index system of the practical effect evaluation of Ideological and political education. According to the data and index system, the paper uses analytic hierarchy process to construct the evaluation model of practical effect of Ideological and political education. So far, the design of the practical effect evaluation model of ideological and political education based on in-depth data mining has been completed. The construction of the experimental link to test the use of this model, the experimental results confirmed that the data processing capability of this model is significantly better than the current model. This model can be used in future research to complete the evaluation process.

Most clustering algorithms have been designed only for pure numerical or pure categorical data sets, while nowadays many applications generate mixed data. It raises the question how to integrate various types of attributes so that one could efficiently group objects without loss of information. It is already well understood that a simple conversion of categorical attributes into a numerical domain is not sufficient since relationships between values such as a certain order are artificially introduced. Leveraging the natural conceptual hierarchy among categorical information, concept trees summarize the categorical attributes. In this paper, we introduce the algorithm ClicoT (CLustering mixed-type data Including COncept Trees) as reported by Behzadi et al. (Advances in Knowledge Discovery and Data Mining, Springer, Cham, 2019) which is based on the minimum description length principle. Profiting of the conceptual hierarchies, ClicoT integrates categorical and numerical attributes by means of a MDL-based objective function. The result of ClicoT is well interpretable since concept trees provide insights into categorical data. Extensive experiments on synthetic and real data sets illustrate that ClicoT is noise-robust and yields well-interpretable results in a short runtime. Moreover, we investigate the impact of concept hierarchies as well as various data characteristics in this paper.