OCA: overlapping clustering application unsupervised approach for data analysis

In this paper, the method for unsupervised learning of finite mixture regression (FMR) models is presented for evaluation using agricultural and emissions data sets. The FMR models can be written as problems with incomplete data, and the expectation–maximization (EM) algorithm can be used to estimate unknown variables. The goals of this research are to find the best clustering model with different sets of training and test data and examine the relationship between crop production index and methane emissions in 22 countries from 1990 to 2019 using FMR. In this study also use machine learning process for a FMR model from real world data. According to the findings, the performance of the random training data (RDM) in time series is preferable to that of the fixed training data (FXM). In addition, both RDM and FXM are capable of classifying the 22 countries into two distinct groups and constructing the parameters for the regression model. However, selecting training and test data will result in a good prediction; it is dependent on the data collected. Picking the right training and test data is crucial for accurate predictions-it all comes down to having good data in the first place.

Section: Data Processing and Data Visualizationmentioning

confidence: 99%

Modeling agricultural and methane emission data: a finite mixture regression approach

Thongnim,

Charoenwanit

2024

“…Fundamentally, these equations are constructed to find samples enclosed in a hyper-sphere between adjacent points. This is inspired by the way overlapping is detected using enclosing circles with maxdist in [30], but modified to detect overlap along representative graph's edges. Figure 4 displays the hyper-sphere according to ( 5) and ( 6).…”

Section: Create Density Representative Graphmentioning

confidence: 99%

A new density core graph-cut class decomposition to improve neural network classification performance

Tantamjarik¹,

Tanprasert²

2022

<span>This research presents a new pre-processed class decomposition technique called density core graph-cut (DCGC). The method uses supervised clustering instead of a traditional unsupervised one to decompose the class. Supervised clustering requires additional label information to function and with that it gains a better understanding of the distribution. DCGC employs nearest neighbors to form a density core graph for each class. Then, the edges of each graph to be removed or cut is identified utilizing class information. Lastly, it yields final clusters by grouping all connected cores and assigning the remaining samples to a cluster where the nearest core belongs. Training neural network classifiers on complex label data will yield a better accuracy with the modified class representation. Intuitively, the decision boundaries separating classes based on the modified labels are less complex, and classifiers’ chance to reach these hyperplanes is higher. The results present that training neural networks using label representations from DCGC significantly helps improve the classification accuracy of neural networks on syntactic datasets as high as 30%. For real-world problems, the experiment presents a mixed result in which some datasets moderately benefit from DCGC.</span>

“…One such data mining technique is Clustering. Clustering refers to a set of objects grouped so that the things in the same group are more similar in some particular manner to each other compared with those in the other group [5]. Multiple research areas apply this technique, specifically data mining, statistical data analysis, machine learning, pattern recognition, image  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the clustering algorithms generate exclusive clusters where each item could be a part of a single cluster only. In the field of medical datasets, specifically real-world data, this contains inherently overlapping information, which applies the method of overlapping clustering that permits one item to be a part of more than one cluster [5]. Another essential data mining issue is outlier detection, which identifies and removes data objects from a given data set.…”

Section: Introductionmentioning

confidence: 99%

Overlapping clustering with k-median extension algorithm: An effective approach for overlapping clustering

Danganan

Arceo

2022

Most natural <span>world data involves overlapping communities where an object may belong to one or more clusters, referred to as overlapping clustering. However, it is worth mentioning that these algorithms have a significant drawback. Since some of the algorithm uses k-means, it also inherits the characteristics of being noise sensitive due to the arithmetic mean value which noisy data can considerably influence and affects the clustering algorithm by biasing the structure of obtained clusters. This paper proposed a new overlapping clustering algorithm named OCKMEx, which uses k-median to identify overlapping clusters in the presence of outliers. This new method aims to determine the insensitivity of the OCKMEx algorithm in locating data points that overlap even with outliers. An experimental evaluation of the algorithm was conducted wherein synthetic datasets served as a data source, and the F1 measure criterion was applied to assess the OCKMEx algorithm performance. Results indicate that the OCKMEx algorithm implementing the use of k-median performed a higher accuracy rate of 100% in identifying data points that overlap even with outliers compared to the existing k-means algorithm. The algorithm exhibited a promising performance in identifying overlapping clusters and was resistant to outliers.</span>