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Related Concept Videos

Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Bar Graph01:07

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Structural Classification of Joints01:20

Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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pV-Diagrams01:18

pV-Diagrams

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The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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Refining Graph Structure for Incomplete Multi-View Clustering.

Xiang-Long Li, Man-Sheng Chen, Chang-Dong Wang

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    This summary is machine-generated.

    This study introduces Graph Structure Refining for Incomplete Multi-View Clustering (GSRIMC), a novel method that avoids feature recovery and refines graph structures to improve clustering performance on incomplete datasets.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Computer Science

    Background:

    • Incomplete Multi-View Clustering (MVC) is a significant challenge.
    • Existing methods often rely on feature recovery, which can introduce errors.
    • Biased errors in graph structures of incomplete data degrade performance.

    Purpose of the Study:

    • To propose a novel graph-based method for incomplete MVC.
    • To overcome limitations of feature recovery and biased errors.
    • To enhance clustering accuracy on incomplete multi-view data.

    Main Methods:

    • Graph Structure Refining for Incomplete MVC (GSRIMC) avoids feature recovery.
    • Separates biased errors from refined graph structures using tensor nuclear norm.
    • Employs cross-view graph learning to capture missing local structures.

    Main Results:

    • GSRIMC demonstrates superior performance compared to state-of-the-art methods.
    • The method effectively handles biased errors in incomplete graph structures.
    • Accurate clustering results are achieved without feature imputation.

    Conclusions:

    • GSRIMC offers an effective solution for incomplete MVC.
    • The proposed method improves robustness and accuracy.
    • It provides a new direction for handling incomplete multi-view data.