Effectiveness Of Clustering Algorithm Research Articles

An inversion-based clustering approach for complex clusters

BackgroundThe choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications.MethodsIn this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index.ResultsThe simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains.ConclusionsThe inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Analysis of the effectiveness of clustering algorithms for multimodal samples using computer simulation of an educational experiment

Introduction. The article is devoted to the problem of primary data processing of pedagogical experiments having a multimodal character. The purpose of the study is to identify the most effective and universal clustering algorithms for pedagogical experiments. Materials and Methods. The study used the method of modeling a pedagogical experiment. The analysis of 5 clustering algorithms is conducted. The effectiveness of clustering algorithms was evaluated based on the proportion of observations with clustering errors at various tolerance levels and the Jacquard similarity coefficient. Regression analysis was used to assess the influence of modeling parameters of a pedagogical experiment and indicators of descriptive statistics on the effectiveness of clustering algorithms. Results. The assessment of the effectiveness of various data clustering algorithms is provided, as well as a correlation and regression analysis of factors affecting clustering efficiency indicators was carried out. Conclusions. The most effective clustering algorithms for multimodal samples include the K-means algorithm and the agglomerative hierarchical algorithm. The results obtained in this research can be used for statistical analysis of pedagogical, psychological, sociological, biological and medical research data.

The effect of clustering algorithms on question answering

Question answering (QA) is one of the essential fields in information retrieval where specific answers are provided instead of large documents. The relations among questions and answers are determined using natural language processing techniques while clustering algorithms can be helpful in improving the effectiveness of result retrieval by reducing the amount of required comparisons for a specific question or answer. In this work, we introduce a clustering-based approach for a QA system. This approach groups related questions into clusters using different clustering algorithms, specifies the appropriate answer using similarity methods between the answers and the generated clusters, and then assigns answers to their most related questions. Different clustering algorithms, such as k-means, spherical k-means, single-linkage hierarchical clustering (SLHA), unweighted pair group method with arithmetic mean (UPGMA), expectation–maximization (EM), and clustering Arabic documents based on bond energy (CADBE), are tested. The effectiveness of a clustering algorithm is investigated with respect to certain factors, including number of clusters, text representation, similarity measure between answers and clusters, and similarity measure between answers and questions in a selected cluster. In addition, a comprehensive ranking system is introduced to evaluate the performance of clustering algorithms. Evaluation is performed using the Dataset of Arabic Why Question Answering System (DAWQAS) and the Multilingual Question Answering (MLQA) dataset. Results show that CADBE achieves the highest accuracy and the first rank, followed by SLHA and UPGMA, while spherical k-means has the lowest rank. The performance of clustering algorithms for MLQA dataset is affected by its characteristics, such as short questions, long and varied answers, and diverse subject domains. Unigram and bigram intersection measures perform well in most cases. Term frequency inverse document frequency representation outperforms word embedding in DAWQAS. Overall, the experiments provide insights into the performance of clustering algorithms in QA systems.

Semi-Supervised Malware Clustering Based on the Weight of Bytecode and API

With the rapid advances of anti-virus and anti-tracking technologies, three aspects in malware clustering need to be improved for effective clustering, i.e., the robustness of features, the accuracy of similarity measurements, and the effectiveness of clustering algorithms. In this paper, we propose a novel malware family clustering approach based on dynamic and static features with their weights. In this approach, we employ a new similarity measurement method based on EMD to improve the accuracy of feature similarities. In addition, to reduce convergence time and improve clustering purity, we design a novel semi-supervised clustering algorithm, termed as S-DBSCAN by involving supervision information into the original algorithm known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The experimental results demonstrate that the proposed approach can correctly and accurately distinguish the samples among various families and achieve outperformed purity with 98.7%.

Appraising Research Direction & Effectiveness of Existing Clustering Algorithm for Medical Data

The applicability and effectiveness of clustering algorithms had unquestioningly benefitted solving various sectors of real-time problems. However, with the changing time, there is a significant change in forms of the data. This paper briefs about the different taxonomies of the clustering algorithm and highlights the frequently used techniques to understand the research popularity. We also discuss the existing direction of the research work and find that still there is a significant amount of open issues when it comes to clustering medical data. We find that existing techniques are quite symptomatic in nature on local problems in clustering while problems associated with complex medical data are yet to be explored by the researchers. We believe that this manuscript will give a good summary of the effectiveness of existing clustering techniques towards medical data as a contribution.

Effect of Clustering Algorithm on Establishing Markov State Model for Molecular Dynamics Simulations.

Recently, the Markov state model has been applied for kinetic analysis of molecular dynamics simulations. However, discretization of the conformational space remains a primary challenge in model building, and it is not clear how the space decomposition by distinct clustering strategies exerts influence on the model output. In this work, different clustering algorithms are employed to partition the conformational space sampled in opening and closing of fatty acid binding protein 4 as well as inactivation and activation of the epidermal growth factor receptor. Various classifications are achieved, and Markov models are set up accordingly. On the basis of the models, the total net flux and transition rate are calculated between two distinct states. Our results indicate that geometric and kinetic clustering perform equally well. The construction and outcome of Markov models are heavily dependent on the data traits. Compared to other methods, a combination of Bayesian and hierarchical clustering is feasible in identification of metastable states.

An Attribute Weighted Fuzzy Clustering Algorithm for Mixed Crime Data

Background/Objectives: The main objective is to design and develop a clustering algorithm for finding similar sub sets from crime data. This paper focuses a method for developing an algorithm and modify the existing technique in three ways, such as i) new attribute weightage scheme instead of IGR, ii) suitability to mixed data and iii) using FCM-based clustering instead of k-means. Methods/Statistical analysis: Generally, the effectiveness of clustering algorithm is completely based on distance matching that finds the similarity between data records and centroid. Giving equal importance for all the attributes is not much effective in clustering process. Instead, attribute weightage could be included in distance matching. A weight vector is generated based on mutual information. The method for attribute weightage is common for both numerical and categorical data. Finally, the grouping of similar sub sets is done based on FCM-based clustering procedure in which the distance matching is carried out based on the attribute weights. Findings: The experimental analysis has done using crime and hepatitis datasets where the performance of the proposed clustering algorithm has been analyzed. Results show that proposed FCM method has good accuracy than the AK-mode. Application/Improvements: Proposed method plays an important role in crime domain for better prediction. Type II fuzzy can also be used for better closeness analysis.

Mining usage scenarios in business processes: Outlier-aware discovery and run-time prediction

A prominent goal of process mining is to build automatically a model explaining all the episodes recorded in the log of some transactional system. Whenever the process to be mined is complex and highly-flexible, however, equipping all the traces with just one model might lead to mixing different usage scenarios, thereby resulting in a spaghetti-like process description. This is, in fact, often circumvented by preliminarily applying clustering methods on the process log in order to identify all its hidden variants. In this paper, two relevant problems that arise in the context of applying such methods are addressed, which have received little attention so far: (i) making the clustering aware of outlier traces, and (ii) finding predictive models for clustering results. The first issue impacts on the effectiveness of clustering algorithms, which can indeed be led to confuse real process variants with exceptional behavior or malfunctions. The second issue instead concerns the opportunity of predicting the behavioral class of future process instances, by taking advantage of context-dependent “non-structural” data (e.g., activity executors, parameter values). The paper formalizes and analyzes these two issues and illustrates various mining algorithms to face them. All the algorithms have been implemented and integrated into a system prototype, which has been thoroughly validated over two real-life application scenarios.

An empirical study of query expansion and cluster-based retrieval in language modeling approach

The term mismatch problem in information retrieval is a critical problem, and several techniques have been developed, such as query expansion, cluster-based retrieval and dimensionality reduction to resolve this issue. Of these techniques, this paper performs an empirical study on query expansion and cluster-based retrieval. We examine the effect of using parsimony in query expansion and the effect of clustering algorithms in cluster-based retrieval. In addition, query expansion and cluster-based retrieval are compared, and their combinations are evaluated in terms of retrieval performance by performing experimentations on seven test collections of NTCIR and TREC.