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| Notation | Representation |
|
| The training set with points and dimensions |
| The data point |
| The -th clusters |
| The sum of squared errors |
| Denotes the -th cluster centroid |
| The number of data points in the -th cluster |
| Denotes the complexity of -means |
| A family of hash functions |
| The probability |
| The similarity between and |
| The maximum load of business hall |
| The proportion of a specific business |
| The average time to process the business |
| The number of business types |
| The ratio of actual daily load to maximum load |
| The working time |
| The number of staffs in the business hall |
| Represents the actual load during peak period of the business hall |
| The ratio of average load to the maximum load |
| The number of working days |
| The actual load trend |
| Denotes the actual load curve fitting function |
| A constant |
| The regression coefficient |
| The proportion of high-value business |
| The high-value business volume |
| The total business volume |
| The high-frequency load |
| The high threshold |
| The low threshold |
| Represent the current time |
| Represent the latest time |
| The low-frequency load |
| The latest high-load interval |
| The latest low-load interval |
| The copy training set |
| The size of training dataset |
| The minimum number data point in one cluster |
| The maximum distance in one cluster |
| A two-dimensional array |
| The nearest neighbor data |
| Calculate the similarity or distance between |
| The number of clusters |
| Distance between and |
| Conduct -means algorithm |
| The -th dataset |
| Represents the sum of rank, which better than the other |
| Opposite to |
| The tight and separative indicator |
| The XB index |
| Objective function |
| The quantitative value of factors |
| The weight |
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