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在商業運作中，聚類分析可以幫助我們發現相似的對象如用戶、商品、市場等，從而進行針對性的營銷策略，提高效率和收益。本文介紹聚合分類函數的基本語法和示例。

背景信息

日志示例和索引示例（clustering_centroids函數和to_cluster_label函數）

索引如圖所示。更多信息，請參見創建索引。

日志樣例如下：

{PushBack"entity_group":"A","entity_id":"0","x0":"9.43755123272417","x1":"10.9239183048056"}
{PushBack"entity_group":"A","entity_id":"1","x0":"9.90934789781198","x1":"10.0410638670737"}
{PushBack"entity_group":"A","entity_id":"2","x0":"10.0237761750608","x1":"11.5427618678773"}
{PushBack"entity_group":"A","entity_id":"3","x0":"8.76413639285441","x1":"9.91100696065114"}
{PushBack"entity_group":"A","entity_id":"4","x0":"9.72866019535702","x1":"10.9289903697879"}
{PushBack"entity_group":"A","entity_id":"5","x0":"9.31732230036184","x1":"9.70753937768216"}
{PushBack"entity_group":"A","entity_id":"6","x0":"10.25881289615","x1":"9.74090182514911"}
{PushBack"entity_group":"A","entity_id":"7","x0":"10.7476586051507","x1":"9.68058142945478"}
{PushBack"entity_group":"A","entity_id":"8","x0":"10.4225378902342","x1":"11.2940460054908"}
{PushBack"entity_group":"A","entity_id":"9","x0":"9.60791396236535","x1":"9.74099863764865"}
{PushBack"entity_group":"A","entity_id":"10","x0":"-9.88083328783069","x1":"9.96483490201365"}
{PushBack"entity_group":"A","entity_id":"11","x0":"-9.2566893228908","x1":"10.4709996742353"}
{PushBack"entity_group":"A","entity_id":"12","x0":"-9.42075011656993","x1":"10.2225398224866"}
{PushBack"entity_group":"A","entity_id":"13","x0":"-11.0476084082138","x1":"9.6602175401245"}
{PushBack"entity_group":"A","entity_id":"14","x0":"-10.4835290932454","x1":"10.0468761053371"}
{PushBack"entity_group":"A","entity_id":"15","x0":"-10.0712229897491","x1":"10.7961735513211"}
{PushBack"entity_group":"A","entity_id":"16","x0":"-9.20579985976076","x1":"11.3595617847332"}
{PushBack"entity_group":"A","entity_id":"17","x0":"-10.2257991823914","x1":"11.5241122586671"}
{PushBack"entity_group":"A","entity_id":"18","x0":"-9.53462858712671","x1":"10.4203828737972"}
{PushBack"entity_group":"A","entity_id":"19","x0":"-9.32911152937254","x1":"8.64624234507702"}
{PushBack"entity_group":"A","entity_id":"20","x0":"10.0383094216576","x1":"-10.8386382322694"}

日志示例和索引示例（clustering函數）

索引如圖所示。更多信息，請參見創建索引。

日志樣例如下：

1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126,83,77,4200,None
2,Male,28,Doctor,6.2,6,60,8,Normal,125,80,75,10000,None
3,Male,28,Doctor,6.2,6,60,8,Normal,125,80,75,10000,None
4,Male,28,Sales Representative,5.9,4,30,8,Obese,140,90,85,3000,Sleep Apnea
5,Male,28,Sales Representative,5.9,4,30,8,Obese,140,90,85,3000,Sleep Apnea
6,Male,28,Software Engineer,5.9,4,30,8,Obese,140,90,85,3000,Insomnia
7,Male,29,Teacher,6.3,6,40,7,Obese,140,90,82,3500,Insomnia
8,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
9,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
10,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
11,Male,29,Doctor,6.1,6,30,8,Normal,120,80,70,8000,None
12,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
13,Male,29,Doctor,6.1,6,30,8,Normal,120,80,70,8000,None
14,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None
15,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None
16,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None
17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132,87,80,4000,Sleep Apnea
18,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,Sleep Apnea
19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132,87,80,4000,Insomnia
20,Male,30,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
21,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
22,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
23,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
24,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
25,Male,30,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
26,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
27,Male,30,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
28,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
29,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
30,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130,86,78,4100,Sleep Apnea
32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130,86,78,4100,Insomnia
33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117,76,69,6800,None
34,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
35,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
36,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
37,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
38,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
39,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
40,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
41,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
42,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
43,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
44,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
45,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
46,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
47,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
48,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
49,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
50,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,Sleep Apnea
51,Male,32,Engineer,7.5,8,45,3,Normal,120,80,70,8000,None
52,Male,32,Engineer,7.5,8,45,3,Normal,120,80,70,8000,None
53,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
54,Male,32,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
55,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
56,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
57,Male,32,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
58,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
59,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
60,Male,32,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
61,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
62,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
63,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
64,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
65,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
66,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118,76,68,7000,None
68,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,Insomnia
69,Female,33,Scientist,6.2,6,50,6,Overweight,128,85,76,5500,None
70,Female,33,Scientist,6.2,6,50,6,Overweight,128,85,76,5500,None
71,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
72,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
73,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
74,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
75,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
76,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
77,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
78,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
79,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
80,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
81,Female,34,Scientist,5.8,4,32,8,Overweight,131,86,81,5200,Sleep Apnea
82,Female,34,Scientist,5.8,4,32,8,Overweight,131,86,81,5200,Sleep Apnea
83,Male,35,Teacher,6.7,7,40,5,Overweight,128,84,70,5600,None
84,Male,35,Teacher,6.7,7,40,5,Overweight,128,84,70,5600,None
85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120,80,70,8000,None
86,Female,35,Accountant,7.2,8,60,4,Normal,115,75,68,7000,None
87,Male,35,Engineer,7.2,8,60,4,Normal,125,80,65,5000,None
88,Male,35,Engineer,7.2,8,60,4,Normal,125,80,65,5000,None
89,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
90,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
91,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
92,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120,80,70,8000,None
94,Male,35,Lawyer,7.4,7,60,5,Obese,135,88,84,3300,Sleep Apnea
95,Female,36,Accountant,7.2,8,60,4,Normal,115,75,68,7000,Insomnia
96,Female,36,Accountant,7.1,8,60,4,Normal,115,75,68,7000,None
97,Female,36,Accountant,7.2,8,60,4,Normal,115,75,68,7000,None
98,Female,36,Accountant,7.1,8,60,4,Normal,115,75,68,7000,None
99,Female,36,Teacher,7.1,8,60,4,Normal,115,75,68,7000,None
100,Female,36,Teacher,7.1,8,60,4,Normal,115,75,68,7000,None
101,Female,36,Teacher,7.2,8,60,4,Normal,115,75,68,7000,None

聚類分析函數列表

聚類分析是一種數據挖掘技術，它可以將數據集中的對象自動地分為不同的組，使得同一組內的對象具有較高的相似性，而不同組之間的對象具有較低的相似性。

函數名稱	語法	說明	返回值類型
clustering_centroids函數	clustering_centroids(array(array(double)) samples, integer num_of_clusters)	通過聚類分析對輸入的對象屬性樣本數據進行分類，用戶可以指定希望聚成的類組數量。在完成聚類處理后，函數將輸出每個類組數據的平均值（類心）。	array(array(double))
to_cluster_label函數	to_cluster_label(array(array(double)) centroids, array(double) attribute_vector)	通過對象的屬性向量及clustering_centroids函數計算的類中心，將對象分配至與其最接近的類組，并返回該類組的數字標識。	long
clustering函數	array(array(varchar)) clustering(array(varchar) attribute_vector, array(varchar) attribute_name_vector, array(varchar) attribute_data_type_vector, integer num_of_clusters)	聚合函數，通過對提供的樣本進行聚類分析，并返回一個二維字符串數組，其中包含各對象及其對應的分類編號。	array(array(varchar))

clustering_centroids函數

聚類是一種無監督學習方法，將數據集分成若干個互不重疊的類，使得同一個類內的數據點相似度較高，而不同類的數據點相似度較低。clustering_centroids函數從輸入的樣本二維數組和聚類個數中，返回類組數據的平均值（類心）。

array(array(double)) clustering_centroids(array(array(double)) samples, long num_of_clusters)

參數

說明

samples

一個二維數組，可以看成一個二維表格，每一行數據構成一個一維數據，表示一個對象的屬性向量。

num_of_clusters

期望聚和成類的個數。

參數返回各個聚類的類心。

使用示例

查詢和分析語句

* | select entity_group,
    clustering_centroids(array_agg(array[x0, x1]), 5) as cluster_centroids
from log
group by entity_group

返回結果

返回由各個聚類的類心組成的二維數組，每個聚類的類心是描述一個屬性向量的一維數組。

entity_group	cluster_centroids
A	[[-9.845597237715113,10.311194085779278],[-9.512707125089744,-9.890571794654335],[9.821771754807048,10.351180864562114],[10.66747750273295,-9.081622556974239],[9.361759158361564,-9.91604553609393]]

to_cluster_label函數

根據clustering_centroids函數獲取類心后，使用to_cluster_label函數將對象歸類到最接近的類中，to_cluster_label函數的返回值是對象的類組的數字編號。

long to_cluster_label(array(array(double)) centroids, array(double) attribute_vector)

參數	說明
`centroids`	函數clustering_centroids返回的類心（二維數組）中。
`attribute_vector`	被歸類的數據集的屬性向量（每個數據點的一組特征或屬性）。

使用示例

查詢和分析語句

* | with centroids as
(
    select entity_group,
        clustering_centroids(array_agg(array[x0, x1]), 5) as cluster_centroids
    from log
    group by entity_group
)
select t1.entity_id,
    t1.entity_group,
    to_cluster_label(t2.cluster_centroids, array[t1.x0, t1.x1]) as cluster_label
from log as t1
    join centroids as t2
    on t1.entity_group = t2.entity_group
order by entity_id
limit 100000

返回結果
cluster_label字段為類組的數字編號。
entity_id
entity_group
cluster_label
0
A
3
1
A
3
2
A
3
...
...
...

clustering函數

聚合函數，通過自動對提供的樣本進行聚類分析，并返回一個二維字符串數組，其中包含各對象及其對應的分類編號。

array(array(varchar)) clustering(array(varchar) attribute_vector, array(varchar) attribute_name_vector, array(varchar) attribute_data_type_vector, integer num_of_clusters)

參數	說明
`attribute_vector`	將一個對象的不同屬性轉換為字符串后，拼裝成一個一維數組。屬性字段的原始類型可以是數字，也可以是離散文本。
`attribute_name_vector`	將屬性字段名組合成一維數組，對象屬性需統一轉換為數字類型并進行歸一化處理，以便在各階段中使用。
`attribute_data_type_vector`	將屬性字段類型整合為一維數組，其中對象屬性統一轉換為數字類型并進行歸一化處理，這些步驟為后續階段的分析提供必要信息。屬性字段類型： `ID_STR`：對應字段是聚類對象的字符串類型的ID。 `ID_NUM`：對應字段是聚類對象的數字類型的ID。 `X_STR_CATEGORICAL`：對應字段是字符串類型的離散型數據，例如某一字段的值僅限于'Male'、'Female'或'Unknown'這幾個固定選項。 `X_NUM_CATEGORICAL`：對應字段是離散型的數據，但是數據類型是數字離散型，例如某一字段的值僅限于1, 2, 3, 4, 5這幾個固定選項。 `X_NUMERIC`：對應字段是連續的數字類型數據。重要 `attribute_vector`、`attribute_name_vector`、`attribute_data_type_vector`三個數組的長度必須保持一致。例如有10個索引字段用于聚類分析，10個索引字段的值構成`attribute_vector`，10個索引字段的名字構成`attribute_name_vector`，10個索引字段對應的類型描述構成`attribute_data_type_vector`。
`num_of_clusters`	可選參數。聚合的類數，如果不指定則會自動選擇。

使用示例

查詢和分析語句

用clustering聚合函數進行分析，然后再用unnest函數展開結果。更多信息，請參見UNNEST子句。

* | with clustering_agg as
(
  select group_id,
  clustering(
    array[cast(person_id as varchar),
          cast(gender as varchar),
          cast(age as varchar),
          cast(occupation as varchar),
          cast(sleep_duration as varchar),
          cast(quality_of_sleep as varchar),
          cast(physical_activity_level as varchar),
          cast(stress_level as varchar),
          cast(bmi_category as varchar),
          cast(blood_pressure_systolic as varchar),
          cast(blood_pressure_diastolic as varchar),
          cast(heart_rate as varchar),
          cast(daily_steps as varchar)], -- 對象屬性內容數組，也包括對象ID字段在內，方便后面unnest函數展開以后再提取這個字段出來
    array['person_id', 'gender', 'age', 'occupation', 'sleep_duration',
          'quality_of_sleep', 'physical_activity_level', 'stress_level',
          'bmi_category', 'blood_pressure_systolic', 'blood_pressure_diastolic',
          'heart_rate', 'daily_steps'], -- 對象屬性字段名稱
    array['ID_NUM',
          'X_STR_CATEGORICAL',
          'X_NUMERIC',
          'X_STR_CATEGORICAL',
          'X_NUMERIC',
          'X_NUMERIC',
          'X_NUMERIC',
          'X_NUMERIC',
          'X_STR_CATEGORICAL',
          'X_NUMERIC',
          'X_NUMERIC',
          'X_NUMERIC',
          'X_NUMERIC'],  -- 對象屬性字段的類型
    5  -- 要聚類成為多少個分組
  ) as clustering_outcome
  from (
    select 'G1' as group_id, -- 加一個group_id，為了展示這是一個聚合函數
        * from  log
  )as sleep_health_group_data
  group by group_id
)

select ca.group_id,
	t. person_cluster[1] as person_id,
	t. person_cluster[2] as cluster_id
from clustering_agg as ca
	cross join unnest(clustering_outcome) as t(person_cluster)

返回結果
group_id
person_id
cluster_id
G1
266
1
G1
268
1
...
...
...

entity_id	entity_group	cluster_label
0	A	3
1	A	3
2	A	3
...	...	...

group_id	person_id	cluster_id
G1	266	1
G1	268	1
...	...	...

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背景信息

日志示例和索引示例（clustering_centroids函數和to_cluster_label函數）

日志示例和索引示例（clustering函數）

聚類分析函數列表

clustering_centroids函數

使用示例

to_cluster_label函數

使用示例

clustering函數

使用示例