Advanced: Clustering
You can cluster the data into separate buckets to find some underlying complexity.
Example
x | y |
---|---|
3.275154 | 2.957587 |
-3.344465 | 2.603513 |
0.355083 | -3.376585 |
1.852435 | 3.547351 |
-2.078973 | 2.552013 |
-0.993756 | -0.884433 |
2.682252 | 4.007573 |
-3.087776 | 2.878713 |
-1.565978 | -1.256985 |
2.441611 | 0.444826 |
-0.659487 | 3.111284 |
-0.459601 | -2.618005 |
2.17768 | 2.387793 |
-2.920969 | 2.917485 |
-0.028814 | -4.168078 |
3.625746 | 2.119041 |
-3.912363 | 1.325108 |
-0.551694 | -2.814223 |
2.855808 | 3.483301 |
-3.594448 | 2.856651 |
0.421993 | -2.372646 |
1.650821 | 3.407572 |
-2.082902 | 3.384412 |
-0.718809 | -2.492514 |
4.513623 | 3.841029 |
-4.822011 | 4.607049 |
-0.656297 | -1.449872 |
1.919901 | 4.439368 |
-3.287749 | 3.918836 |
-1.576936 | -2.977622 |
3.598143 | 1.97597 |
-3.977329 | 4.900932 |
-1.79108 | -2.184517 |
3.914654 | 3.559303 |
-1.910108 | 4.166946 |
-1.226597 | -3.317889 |
1.148946 | 3.345138 |
-2.113864 | 3.548172 |
0.845762 | -3.589788 |
2.629062 | 3.535831 |
-1.640717 | 2.990517 |
-1.881012 | -2.485405 |
4.606999 | 3.510312 |
-4.366462 | 4.023316 |
0.765015 | -3.00127 |
3.121904 | 2.173988 |
-4.025139 | 4.65231 |
-0.559558 | -3.840539 |
4.376754 | 4.863579 |
-1.874308 | 4.032237 |
-0.089337 | -3.026809 |
3.997787 | 2.518662 |
-3.082978 | 2.884822 |
0.845235 | -3.454465 |
1.327224 | 3.358778 |
-2.889949 | 3.596178 |
-0.966018 | -2.839827 |
2.960769 | 3.079555 |
-3.275518 | 1.577068 |
0.639276 | -3.41284 |
sqlseal
TABLE clustering = table(0)
ADVANCED MODE
CHART
const datasetArray = data.map(d => ([d.x, d.y]))
var CLUSTER_COUNT = 6;
var DIENSIION_CLUSTER_INDEX = 2;
var COLOR_ALL = [
'#37A2DA',
'#e06343',
'#37a354',
'#b55dba',
'#b5bd48',
'#8378EA',
'#96BFFF'
];
var pieces = [];
for (var i = 0; i < CLUSTER_COUNT; i++) {
pieces.push({
value: i,
label: 'cluster ' + i,
color: COLOR_ALL[i]
});
}
return {
dataset: [
{
source: datasetArray,
id: 'data'
},
{
transform: {
type: 'ecStat:clustering',
print: true,
config: {
clusterCount: CLUSTER_COUNT,
outputType: 'single',
outputClusterIndexDimension: DIENSIION_CLUSTER_INDEX
}
}
}
],
tooltip: {
position: 'top'
},
visualMap: {
type: 'piecewise',
top: 'middle',
min: 0,
max: CLUSTER_COUNT,
left: 10,
splitNumber: CLUSTER_COUNT,
dimension: DIENSIION_CLUSTER_INDEX,
pieces: pieces
},
grid: {
left: 120
},
xAxis: {},
yAxis: {},
series: {
type: 'scatter',
encode: { tooltip: [0, 1] },
symbolSize: 15,
itemStyle: {
borderColor: '#555'
},
datasetIndex: 1
}
};
SELECT * FROM clustering