This paper proposes a parallel solution for mining maximal quasi-cliques that is able to fully utilize CPU cores. However, mining maximal quasi-cliques is notoriously expensive, and even a recent algorithm for mining large maximal quasi-cliques is flawed and can lead to a lot of repeated searches. Quasi-clique is a natural definition for dense structures, so finding large and hence statistically significant quasi-cliques is useful in applications such as community detection in social networks and discovering significant biomolecule structures and pathways. Select the variables to be included in the chart.Given a user-specified minimum degree threshold \(\gamma \), a \(\gamma \)-quasi-clique is a subgraph where each vertex connects to at least \(\gamma \) fraction of the other vertices. Filter your data by clicking the down arrow next to Filter.In this example, the observations for each year have been giving a different color. Click the down arrow beneath Color By to add a color to specified variable.
Then click Explore – Chart Wizard and select Parallel Coordinates.
Parallel miner tv#
Using Analytic Solver Cloud to Create a Parallel Coordinates ChartĬlick Help – Example Models – Forecasting / Data Mining Models in Analytic Solver Cloud to open the Sports TV Ratings dataset. To reopen the chart, on the Data Mining ribbon, from the Data Analysis tab, select Explore - Existing Charts - Parallel. For this example, type Parallel for the chart name, then click Save. To cancel the save and return to the chart, click Cancel. To save the chart for later viewing, click Save. To exit the graph, click the red X in the upper right-hand corner of the Chart Wizard Window. To add a variable to the matrix, check the desired variable under Filters. To remove a variable from the matrix, uncheck the desired variable under Filters. In these same years, the ratings for the Daytona 500 was correspondingly lower. There are just four years wheree high ratings for the Indy 500 was recorded. When looking at the observations for each feature, this chart shows that in most years, the viewship of the Indy 500 was low whereas the viewship for the Daytona 500 was high. As a result, this chart already conveys that the viewership for the Daytona 500 is larger than the viewship for the Indy 500. The range of ratings for the Indy 500 has a high of 10.9 and a low of 2.3 whereas the range for the Daytona 500 is 11.3 to 4.4. The first thing that we notice is the range of each of the races that are indicated at the top and bottom of each vertical line. Select two variables, Indy 500 and Daytona 500. Click Finish to draw the plot. The Chart Wizard - Variable Selection dialog opens. Select Parallel Coordinates, and click Next.
Select a cell within the dataset, say A2, then click Explore – Chart Wizard on the Data Mining ribbon. Observations that are contained within the same record are connected by a line.Ĭlick Help – Examples on the Data Mining ribbon to open the example dataset, SportsTVRatings.xlsx. Observations for each feature are recorded as dots on the vertical line. This type of graph starts with a set of vertically drawn parallel lines, equally spaced, which corresponds to the features included in the graph.
The example below illustrates the use of Analytic Solver Data Mining’s chart wizard in drawing a Parallel Coordinates Plot using the dataset.Ī parallel coordinates plot allows the exploration of high dimensional datasets, or datasets with a large number of features (variables).
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