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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Construction of Frequency Distribution01:15

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A frequency distribution table can be constructed using the steps given below.
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Probability Histograms01:17

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Multiple Bar Graph01:07

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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.
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Expected Frequencies in Goodness-of-Fit Tests01:19

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Related Experiment Video

Updated: Jul 4, 2025

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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Generalizing Design of Support Measures for Counting Frequent Patterns in Graphs.

Jinghan Meng1, Napath Pitaksirianan1, Yicheng Tu1

  • 1Dept. of Computer Science, University of South Florida, Tampa, Florida, USA.

Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data
|February 7, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces general conditions for creating new frequent subgraph mining (FSM) support measures. These conditions generalize existing measures and introduce a new one, bridging computational gaps.

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Area of Science:

  • Computer Science
  • Graph Theory
  • Data Mining

Background:

  • Frequent subgraph mining (FSM) is crucial in data analysis.
  • Developing effective support measures for FSM patterns is a key challenge.
  • Current methods often use hypergraph frameworks, unifying overlap-graph (MIS) and minimum image/instance (MNI) measures.

Purpose of the Study:

  • To explore the space between existing FSM support measure types.
  • To provide general sufficient conditions for designing novel FSM support measures within a hypergraph framework.
  • To guide the development of new, efficient, and user-defined support measures.

Main Methods:

  • Developed general sufficient conditions for constructing support measures in a hypergraph framework.
  • Applied these conditions to generalize existing measures like Minimum Image/Instance (MNI) and Minimum Instance (MI).
  • Introduced a novel Maximum Independent Subedge Set (MISS) measure.

Main Results:

  • The proposed conditions successfully generalize MNI and MI measures, enabling user-defined linear-time measures.
  • The new Maximum Independent Subedge Set (MISS) measure effectively bridges the gap between MIS and MI measures in terms of computational complexity and support count.
  • Demonstrated applicability to measures beyond the overlap graph framework.

Conclusions:

  • The established sufficient conditions offer a robust framework for creating new FSM support measures.
  • The generalized and novel measures enhance the flexibility and efficiency of frequent subgraph mining.
  • The MISS measure provides a valuable new option for balancing computational cost and pattern frequency in FSM.