Modified LDA Approach For Cluster Based Gene Classification Using K-Mean Method

Joshi, Reva; Prasad, Ram; Mewada, Pradeep; Saurabh, Praneet

doi:10.1016/j.procs.2020.04.270

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Linear discriminant analysis (LDA) is also known as Fisher discriminant analysis and class-based KLT [8]. Tis method selects the features that can best separate all kinds of data in the sense of least mean square so that the samples are as far away from each other in the feature space as possible and within the class as compact as possible, so that the sample has the best separability [28][29][30].…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Jiang,

Ji,

Yang

et al. 2023

Shock and Vibration

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Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis.

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Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Jiang,

Ji,

Yang

et al. 2023

Shock and Vibration

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“…, numbers of desired clusters are F and output membership matrix V = {v if } B,F i,f=1 with weight Z g S g=1 for kernels. The multiple kernel fuzzy c-means have the following steps: (9) )…”

Section: Multiple Kernel Fuzzy C-means Clusteringmentioning

confidence: 99%

“…LDA performs better in the classification of clinical reports [8]. LDA is used in a various applications, including the classification of genome sequence [9], the discovery of discussion concepts in social networks [10], patient data modeling [11], topic extraction from medical reports [12], the discovery of scientific data and biomedical relationships [13,14]. The LDA method finds important clinical problems and formats clinical text reports in another investigation [15].…”

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confidence: 99%

A novel multiple kernel fuzzy topic modeling technique for biomedical data

et al. 2022

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Background Text mining in the biomedical field has received much attention and regarded as the important research area since a lot of biomedical data is in text format. Topic modeling is one of the popular methods among text mining techniques used to discover hidden semantic structures, so called topics. However, discovering topics from biomedical data is a challenging task due to the sparsity, redundancy, and unstructured format. Methods In this paper, we proposed a novel multiple kernel fuzzy topic modeling (MKFTM) technique using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm for biomedical text mining. In detail, the proposed fusion probabilistic inverse document frequency method is used to estimate the weights of global terms while MKFTM generates frequencies of local and global terms with bag-of-words. In addition, the principal component analysis is applied to eliminate higher-order negative effects for term weights. Results Extensive experiments are conducted on six biomedical datasets. MKFTM achieved the highest classification accuracy 99.04%, 99.62%, 99.69%, 99.61% in the Muchmore Springer dataset and 94.10%, 89.45%, 92.91%, 90.35% in the Ohsumed dataset. The CH index value of MKFTM is higher, which shows that its clustering performance is better than state-of-the-art topic models. Conclusion We have confirmed from results that proposed MKFTM approach is very efficient to handles to sparsity and redundancy problem in biomedical text documents. MKFTM discovers semantically relevant topics with high accuracy for biomedical documents. Its gives better results for classification and clustering in biomedical documents. MKFTM is a new approach to topic modeling, which has the flexibility to work with a variety of clustering methods.

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“…A model using a parallel approach is implemented to cluster the multiple document collections [10]. The key issue is to find automatic document clusters in large text corpus and it is very high cost to compare documents in a high dimensional vector space.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

Devi¹,

Kumar²

2020

IJACSA

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As the size of the document collections are increasing day-by-day, finding an essential document clusters for classification problem is one of the major problem due to high inter and intra document variations. Also, most of the conventional classification models such as SVM, neural network and Bayesian models have high true negative rate and error rate for document classification process. In order to improve the computational efficacy of the traditional document classification models, a hybrid feature extraction-based document cluster approach and classification approaches are developed on the large document sets. In the proposed work, a hybrid glove feature selection model is proposed to improve the contextual similarity of the keywords in the large document corpus. In this work, a hybrid document clustering similarity index is optimized to find the essential key document clusters based on the contextual keywords. Finally, a hybrid document classification model is used to classify the clustered documents on large corpus. Experimental results are conducted on different datasets, it is noted that the proposed document clustering-based classification model has high true positive rate, accuracy and low error rate than the conventional models.

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Modified LDA Approach For Cluster Based Gene Classification Using K-Mean Method

Cited by 9 publications

References 18 publications

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

A novel multiple kernel fuzzy topic modeling technique for biomedical data

A Hybrid Document Features Extraction with Clustering based Classification Framework on Large Document Sets

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