Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Sriwanna, Kittakorn; Boongoen, Tossapon; Iam-On, Natthakan

doi:10.1186/s13673-017-0103-8

Cited by 19 publications

(8 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, an investigation of generating those using quality-diversity based selection of ensemble members [48] and noise-induced ensemble generation [49] can be truly useful in practice. Besides, possible applications of imputation techniques to fuzzy reasoning [50] and clustering-based data discretization [51] can also be further studied.…”

Section: Resultsmentioning

confidence: 99%

Improved KNN Imputation for Missing Values in Gene Expression Data

Keerin¹,

Boongoen²

2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The problem of missing values has long been studied by researchers working in areas of data science and bioinformatics, especially the analysis of gene expression data that facilitates an early detection of cancer. Many attempts show improvements made by excluding samples with missing information from the analysis process, while others have tried to fill the gaps with possible values. While the former is simple, the latter safeguards information loss. For that, a neighbour-based (KNN) approach has proven more effective than other global estimators. The paper extends this further by introducing a new summarization method to the KNN model. It is the first study that applies the concept of ordered weighted averaging (OWA) operator to such a problem context. In particular, two variations of OWA aggregation are proposed and evaluated against their baseline and other neighbor-based models. Using different ratios of missing values from 1%-20% and a set of six published gene expression datasets, the experimental results suggest that new methods usually provide more accurate estimates than those compared methods. Specific to the missing rates of 5% and 20%, the best NRMSE scores as averages across datasets is 0.65 and 0.69, while the highest measures obtained by existing techniques included in this study are 0.80 and 0.84, respectively.

show abstract

Section: Resultsmentioning

confidence: 99%

Improved KNN Imputation for Missing Values in Gene Expression Data

Keerin¹,

Boongoen²

2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…The application of PIC as a consensus function of link-based cluster ensembles is a crucial step towards making the proposed approach truly effective in terms of run-time and quality. Other possible future works include the use of proposed method to support accurate clusterings for fuzzy reasoning [49], handling of data with missing values [50] and data discretization [51].…”

Section: Discussionmentioning

confidence: 99%

Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

Boongoen¹,

Iam-On²

2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

A mix between numerical and nominal data types commonly presents many modern-age data collections. Examples of these include banking data, sales history and healthcare records, where both continuous attributes like age and nominal ones like blood type are exploited to characterize account details, business transactions or individuals. However, only a few standard clustering techniques and consensus clustering methods are provided to examine such a data thus far. Given this insight, the paper introduces novel extensions of link-based cluster ensemble, LCE WCT and LCE WTQ that are accurate for analyzing mixed-type data. They promote diversity within an ensemble through different initializations of the k-prototypes algorithm as base clusterings and then refine the summarized data using a link-based approach. Based on the evaluation metric of NMI (Normalized Mutual Information) that is averaged across different combinations of benchmark datasets and experimental settings, these new models reach the improved level of 0.34, while the best model found in the literature obtains only around the mark of 0.24. Besides, parameter analysis included herein helps to enhance their performance even further, given relations of clustering quality and algorithmic variables specific to the underlying link-based models. Moreover, another significant factor of ensemble size is examined in such a way to justify a tradeoff between complexity and accuracy.

show abstract

“…Besides, another future work is to explore optimization techniques found in the literature [56] that may lead to a better selection of centroids from a pool of multiple clusterings. This is similar to the attempt to improve a greedy optimization to discretization of feature domains [64]. Finally, an introduction of fuzzy sets and vocabularies is able to support the explainability of prediction process [21].…”

Section: Discussionmentioning

confidence: 92%

Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem

Pimsarn

Boongoen

Iam-On

et al. 2022

Complex Intell. Syst.

Self Cite

View full text Add to dashboard Cite

Most defence mechanisms such as a network-based intrusion detection system (NIDS) are often sub-optimal for the detection of an unseen malicious pattern. In response, a number of studies attempt to empower a machine-learning-based NIDS to improve the ability to recognize adversarial attacks. Along this line of research, the present work focuses on non-payload connections at the TCP stack level, which is generalized and applicable to different network applications. As a compliment to the recently published investigation that searches for the most informative feature space for classifying obfuscated connections, the problem of class imbalance is examined herein. In particular, a multiple-clustering-based undersampling framework is proposed to determine the set of cluster centroids that best represent the majority class, whose size is reduced to be on par with that of the minority. Initially, a pool of centroids is created using the concept of ensemble clustering that aims to obtain a collection of accurate and diverse clusterings. From that, the final set of representatives is selected from this pool. Three different objective functions are formed for this optimization driven process, thus leading to three variants of FF-Majority, FF-Minority and FF-Overall. Based on the thorough evaluation of a published dataset, four classification models and different settings, these new methods often exhibit better predictive performance than its baseline, the single-clustering undersampling counterpart and state-of-the-art techniques. Parameter analysis and implication for analyzing an extreme case are also provided as a guideline for future applications.

show abstract

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Cited by 19 publications

References 62 publications

Improved KNN Imputation for Missing Values in Gene Expression Data

Improved KNN Imputation for Missing Values in Gene Expression Data

Using Link-Based Consensus Clustering for Mixed-Type Data Analysis

Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem

Contact Info

Product

Resources

About