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Utilizing the Similarity Meaning of Label in Class Cohesion Calculation

Bayu Priyambadha, Tetsuro Katayama, Yoshihiro Kita, Hisaaki Yamaba, Kentaro Aburada, Nanoubu Okazaki
2020 Journal of Robotics, Networking and Artificial Life (JRNAL)  
The experimental results declared an increase in the value of cohesion produced in line with the similarity of meaning.  ...  The measurement of the value of cohesion is done by looking at the correlation between attributes and methods that are in a class.  ...  [12] , Dijkman defines a formula for calculating the similarity between the two labels or sentence by considering the similarity of meanings (synonyms).  ... 
doi:10.2991/jrnal.k.201215.013 fatcat:7nmn7jrojjg5xjdv4ftcqbuusm

D2C-Based Hybrid Network for Predicting Group Cohesion Scores

Dang Xuan Tien, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim
2021 IEEE Access  
This is one of the first studies that utilizes machine learning to predict group cohesion of a group of people in videos.  ...  Group cohesiveness represents the bonding of people in a group, where higher cohesiveness means stronger bonding among group members.  ... 
doi:10.1109/access.2021.3088340 fatcat:hvkr4kf5mbfaxptwp4lqntcwt4

A Rough Set Approach for Customer Segmentation

Prabha Dhandayudam, Ilango Krishnamurthi
2014 Data Science Journal  
This technique clusters the customers in such a way that the customers in one group behave similarly when compared to the customers in other groups.  ...  The proposed algorithm is compared with other RST based clustering algorithms, such as MMR (Min-Min Roughness), MMeR (Min Mean Roughness), SDR (Standard Deviation Roughness), SSDR (Standard deviation of  ...  In a real data set, because the class label is not known, cohesion and coupling are used to test the quality of the clusters.  ... 
doi:10.2481/dsj.13-019 fatcat:ipkxy4vntjhuldcihxzyds7ynq

Keyword Extraction using Semantic Analysis

Mohamed H.Haggag
2013 International Journal of Computer Applications  
Provision of the overall terms similarity is crucial for defining relevant keywords that most expressing the text in both frequency and weighted likeness.  ...  Document terms are assigned a weighted metric based on the likeness of their meaning content.  ...  Given a set of labeled documents belonging to I classes, assume the clustering algorithm partitions them into J clusters.  ... 
doi:10.5120/9889-4445 fatcat:c7sr5e6pw5hp5hkpxmw6ogqz5u

Metrics Threshold Analysis On the Basis of Clustering Technique for Fault Prediction

2016 International Journal of Science and Research (IJSR)  
The result obtain in this work demonstrate the effectiveness of metrics thresholds.  ...  This work is validated when the fault labels are unavailable and there is a need to check the accuracy of the software.  ...  In first step clusters of original data are formed by utilizing k-mean clustering method.  ... 
doi:10.21275/v5i6.nov164130 fatcat:2vbd6k373nc2tm3je5w7gejjgy

A Deep-Learning-Based Fashion Attributes Detection Model [article]

Menglin Jia, Yichen Zhou, Mengyun Shi, Bharath Hariharan
2018 arXiv   pre-print
Analyzing fashion attributes is essential in the fashion design process.  ...  Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.  ...  Then these labels are used to calculate AP per class and weighted mAP (concatenating all labels regardless of classes); (ii) CorLoc per class and weighted mean CorLoc.  ... 
arXiv:1810.10148v1 fatcat:alqorfbp7bew3luizocz7yiaay

Using the revised EM algorithm to remove noisy data for improving the one-against-the-rest method in binary text classification

Hyoungdong Han, Youngjoong Ko, Jungyun Seo
2007 Information Processing & Management  
But, this one-against-the-rest method has a problem. That is, the documents of a negative data set are not labeled manually, while those of a positive set are labeled by human.  ...  The results of our experiments showed that our method achieved better performance than the original one-against-the-rest method in all the data sets and all the classifiers used in the experiments.  ...  The cohesion within a negative data set in formula (5) is calculated by averaging cosine similarity values betweenC 2ðnegativeÞ and each document of the negative data set, and the cohesion between positive  ... 
doi:10.1016/j.ipm.2006.11.003 fatcat:fk5vjrekrnaxxbc6v22rm225yy

Discovery of small protein complexes from PPI networks with size-specific supervised weighting

Chern Yong, Osamu Maruyama, Limsoon Wong
2014 BMC Systems Biology  
SSS uses a naive-Bayes maximum-likelihood model to weight the edges with two posterior probabilities: that of being in a small complex, and of being in a large complex.  ...  The prediction of small complexes is especially susceptible to noise (missing or spurious interactions) in the PPI network, while smaller groups of proteins are likelier to take on topological characteristics  ...  is sm − comp)P(sm − comp) class∈{sm−comp,lg−comp,non−comp} i P(Fi = fi|(a, b) is class)P(class) The posterior probabilities are calculated in a similar fashion for the other two classes lg-comp and non-comp  ... 
doi:10.1186/1752-0509-8-s5-s3 pmid:25559663 pmcid:PMC4305982 fatcat:7qgspney2jfb7cxypbccr6hw2q

A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery

Yixuan Zhang, Rui Yang, Mengjie Huang, Yu Han, Yiqi Wang, Yun Di, Dongke Su, Qidong Lu, Jun Zhu
2021 Shock and Vibration  
The BP-MLL neural network is utilized for fault diagnosis by classifying the feature vectors.  ...  An effective global error function is proposed in BP-MLL neural network by modifying distance function to improve both generalization ability and fault diagnostic ability of full-labeled and nonlabeled  ...  In Figure 3 , the intracategory standard deviation in class 3 and class 4 are easily overlapped and the distances between the points in each class are similar, resulting in a small average intercategory  ... 
doi:10.1155/2021/7469691 fatcat:wepy5sl7urfqrkeisszjdzwdum

Knowledge Discovery of Service Satisfaction Based on Text Analysis of Critical Incident Dialogues and Clustering Methods

Charles Trappey, Hsin-Ying Wu, Kuan-Liang Liu, Feng-Teng Lin
2013 2013 IEEE 10th International Conference on e-Business Engineering  
, and derives meaningful trends, baselines, and interpretations of consumer satisfaction and dissatisfaction.  ...  A case study of consumers riding the Kaohsiung Mass Rapid Transit System (KMRT) was cased to evaluate the proposed analysis process.  ...  In order to decide the number of clusters within each set, we implemented an iteration process to calculate a clustering quality index composed of cohesion and separation statistics.  ... 
doi:10.1109/icebe.2013.40 dblp:conf/icebe/TrappeyWLL13 fatcat:euuvknbqvjbr3mvfxtihbrc5oe

Adaptive Image Stream Classification via Convolutional Neural Network with Intrinsic Similarity Metrics [article]

Yang Gao, Swarup Chandra, Zhuoyi Wang, Latifur Khan
2018 arXiv   pre-print
Particularly, we utilize a convolution neural network layer that aids in the learning of a latent feature space suitable for novel class detection.  ...  Importantly, they rely on the property of cohesion and separation among instances in feature space.  ...  However, the cohesion property of clusters indicates that instances within a cluster are semantically similar to each Randomly sample a instance x c i ∈ C i . 5: Request truth label y c i of x c i .  ... 
arXiv:1810.03966v1 fatcat:pafh5kzguvb5tmg33rnvhwpfee

A Microservice Decomposition Method Through Using Distributed Representation of Source Code

Omar Al-Debagy, Peter Martinek
2021 Scalable Computing : Practice and Experience  
The proposed method showed promising results in terms of cohesion when compared to other decomposition methods. The proposed method scored better scores in 5 out of 8 tests compared to other methods.  ...  The quality characteristics of the results were measured using two metrics for measuring cohesion. These metrics were Cohesion at Message Level (CHM) and Cohesion at Domain Level (CHD).  ...  Also, we utilized other metrics to compare the sizes of the mentioned applications in the test, such as the number of classes, number of methods, lines of code (LoC), and the number of microservices.  ... 
doi:10.12694/scpe.v22i1.1836 fatcat:ics5ikuowjdr7fjbxejw7uko2y

Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction

Syed Rashid Aziz, Tamim Ahmed Khan, Aamer Nadeem
2021 PeerJ Computer Science  
The quality assurance practitioners can benefit from the utilization of metrics associated with inheritance for labeling datasets and clusters.  ...  The absence of training data and mechanism to labeling a cluster faulty or fault-free is a topic of concern in software fault prediction (SFP).  ...  Since, classification utilizes labels of the class for training, whereas these labels do not utilize in the clustering rather attempt to find out the similarity among the features (Gan, Ma & Wu, 2007)  ... 
doi:10.7717/peerj-cs.722 pmid:34805500 pmcid:PMC8576544 fatcat:y4mjehwcebfodgiffy2l6pt4q4

A Robust Framework for Classifying Evolving Document Streams in an Expert-Machine-Crowd Setting [article]

Muhammad Imran, Sanjay Chawla, Carlos Castillo
2016 arXiv   pre-print
An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date.  ...  of the categorization process.  ...  The cohesiveness of a cluster C i is defined as a combination of the cluster intra-similarity and its inter-similarity with other clusters: intra(C i )/ inter(C i , C c i ).  ... 
arXiv:1610.01858v1 fatcat:vazltaafsbdoxdo2sdmaiitesa

Güncellik Sıklık Parasallık Modeline Dayalı Müşteri Bölümlendirme: E-Perakende Sektöründe Bir Uygulama

2020 Bilişim Teknolojileri Dergisi  
Marketing studies have often drawn attention to the importance of customers for businesses that aim to endure in a harsh competitive environment.  ...  A practical implication of the CRM approach is the analysis of customer data to extract value for businesses, as well as customers.  ...  As noticed in [29] , the objective of clustering is to create non-similar classes where interclass similarity is high.  ... 
doi:10.17671/gazibtd.570866 fatcat:nijhaq5fk5ditpdojpydf7nm4a
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