Euclidean distance compares the straight-line distance between two visualizations whereas the other algorithm tries to take into account shapes or temporal lags by transforming the visualization before calculating the distance, as shown here.
The aggregation function describes how data is aggregated along the y axis.
KMeans clustering is used for identifying represenatitive trends of the whole dataset. This option specifies the number of clusters to be displayed in the representative trends.
Number of Results
Specifies the minimum similarity score for a visualization to be displayed in the query results panel.
When your data is noisy, the visualization pattern can be overwhelmed by noise, which can lead to bad pattern matching. Smoothing tries to capture important features in the data while lessening the noise. Different methods of smoothing are available through the dropdown, and the magnitude of smoothing can be changed through the slider.
Specify a filter that can be applied globally to a dataset to narrow the search. Multiple conditions can be accepted using AND, OR.
1. Consider x-range: Queries will find patterns that happen at similar x ranges. When turned off, the location of the pattern along the x-axis will not affect pattern search.
2. Show original sketch: Shows the original query pattern as a pale green line on top of the visualization results, checked by default.
3. Show scatter plot: Showing the raw data points as a scatterplot.
4. Reverse Y axis: Flipping the y-axis of all visualizations.
Relationships in the form of mathematical equations can be used to query for patterns.
Download Query Results
Number of results:
Minimum similarity threshold:
Export y only
Include Query Pattern
Download all data
Download Clustering Results
Number of Clusters:
Download Outlier Results
Number of Results:
Minimum dissimilarity threshold:
Export y only