Introduction:
Machine learning algorithms such as linear regression, logistic regression, decision tree are very popular topic in today’s market . This article focused on anomaly detection with kmeans algorithm by using outliers.
Kmeans algorithm may be used on diverse of use case scenarios from image compression to system monitoring applications. Identifying anomaly in daily CPU resource utilization trends can be given as a sample usecase. Traditional system monitoring applications use fixed threshold values to identify a bottleneck over the system. But regarding today’s requirements, it is not enough to monitor utilizations with fixed thresholds values. Monitoring system should to learn system resource utilization trends by using ML algorithms and create an alert in anomaly situation.
KMeans is a suitable solution for detecting anomalies in large data sets. Similarity analysis can be given as an example to detect anomalies in a dataset. In the example, two group of cars formed as two different clusters. Those clusters will have its own members in its immediate vicinity that is close similarity groups. If the element is close to the center of the cluster it becomes a group member. Elements far from the center, it identifies an anomaly situation for that cluster. If there is low similarity than those clusters, elements outside the clusters evaluates as an anomaly form.
Kmeans algorithm is basically based on Euclid calculation to measure distance of a tuple to centroid. This calculation repeating until distance values are being stable for each tuple. Please find kMeans visual sample on figure 1;
Figure 1
kMeans algorithm steps performed as below;
1 Assign each tuple to a randomly selected set
2 Calculate the centroid's distance from each cluster
3 Loop until there is no improvement or you reach the maxcount variable
4 Assign each tuple to the most appropriate cluster
5 Update cluster centroid
6 End loop
7 Return cluster information
HANA has its own kmeans algorithm implementation in PAL library. This algorithm can be used easily by any kind of application that running a HANA system.
This article focused on kMeans outliers ABAP implementation without using HANA PAL library. Class has two main public functions. First one is “cluster” method. Second one is “outlier” method. In the cluster method, kMeans attribute values can be provided and assigned dynamically. It can have as many attributes as problem needs.
Dictionary objects:
Before coding, data types need to be defined at ABAP dictionary level.
Table types:
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Structures:
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Test Program:
Dictionary objects figure 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12 ,13 should be created manually. Please find test program, below;
REPORT zkmeans_test.
TYPES: tt_atts TYPE STANDARD TABLE OF string WITH EMPTY KEY,
tt_rawdata TYPE TABLE OF zks_col_kmeans_rawdata WITH EMPTY KEY.
DATA: clustering TYPE REF TO zks_col_kmeans_clustering_tt,
outlier TYPE REF TO zks_col_kmeans_outlier_tt,
numattributes TYPE i,
numclusters TYPE i,
maxcount TYPE i,
lr_kmeans TYPE REF TO zcl_kmeans,
dataset TYPE tt_rawdata.
DATA(attributes) = VALUE tt_atts(
( `SampleAttribute1` )
).
dataset = VALUE #( ( table = VALUE #( ( value = 100 ) ) )
( table = VALUE #( ( value = 99 ) ) )
( table = VALUE #( ( value = 98 ) ) )
( table = VALUE #( ( value = 70 ) ) )
( table = VALUE #( ( value = 95 ) ) )
( table = VALUE #( ( value = 77 ) ) )
( table = VALUE #( ( value = 100 ) ) )
( table = VALUE #( ( value = 97 ) ) )
( table = VALUE #( ( value = 99 ) ) )
).
numattributes = lines( attributes ).
numclusters = 2.
maxcount = 30.
CREATE OBJECT lr_kmeans.
clustering = lr_kmeans>cluster(
rawdata = dataset
numclusters = numclusters
numattributes = numattributes
maxcount = maxcount ).
outlier = lr_kmeans>outlier(
rawdata = dataset
clustering = clustering
numclusters = numclusters
cluster = 0 ).
In example above, only one attribute with a few data provided into the cluster method. Sample test program source code has only one centroid to make it easy to understand. Of course this value can be increased regarding the requirements. Max count value has been set to 30, in test program. But the higher this value, the better results it produces. In the example, in order to identifiy outlier values on cluster, outlier method has been called after cluster method. As soon as outlier values has been identified by the outlier method, those values can be dropped out from provided dataset to filter noisy values.
Class Implementation:
There are two public methods on ABAP ZCL_KMEANS class; cluster and outlier. Other methods are utility functions that help to execute kMeans algorithm. Please find method signatures and implementations by description;
Name 
CLUSTER 
Description 
Assign cluster node id to items in dataset 
Level 
Instance Method 
Visibility 
Public 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
RAWDATA 
Importing 

Type 
ZKS_COL_KMEANS_RAWDATA_TT 
NUMCLUSTERS 
Importing 

Type 
INT4 
NUMATTRIBUTES 
Importing 

Type 
INT4 
MAXCOUNT 
Importing 

Type 
INT4 
CLUSTERING 
Returning 
X 
Type Ref To 
ZKS_COL_KMEANS_CLUSTERING_TT 
METHOD cluster.
DATA: changed TYPE boolean VALUE abap_true,
ct TYPE i VALUE 0,
numtuples TYPE i VALUE 0,
means TYPE REF TO zks_col_kmeans_means_tt,
centroids TYPE REF TO zks_col_kmeans_centroids_tt.
FIELDSYMBOLS: <lr_rawdata> TYPE zks_col_kmeans_rawdata_tt.
numtuples = lines( rawdata ).
clustering = initclustering( numtuples = numtuples
numclusters = numclusters
randomseed = 0 ).
means = allocatemeans( numclusters = numclusters
numattributes = numattributes ).
centroids = allocatecentroids( numclusters = numclusters
numattributes = numattributes ).
ASSIGN rawdata TO <lr_rawdata>.
updatemeans( rawdata = <lr_rawdata>
clustering = clustering
means = means ).
updatecentroids( rawdata = rawdata
clustering = clustering
means = means
centroids = centroids ).
WHILE changed = abap_true AND ct < maxcount.
ct = ct + 1.
changed = Assign( rawData = rawData
clustering = clustering
centroids = centroids ).
UpdateMeans( rawData = rawData
clustering = clustering means = means ).
UpdateCentroids( rawData = rawData
clustering = clustering
means = means
centroids = centroids ).
ENDWHILE.
ENDMETHOD.

Name 
OUTLIER 
Description 
Identify and return anomaly data in provided dataset 
Level 
Instance Method 
Visibility 
Public 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
RAWDATA 
Importing 

Type 
ZKS_COL_KMEANS_RAWDATA_TT 
CLUSTERING 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CLUSTERING_TT 
NUMCLUSTERS 
Importing 

Type 
INT4 
CLUSTER 
Importing 

Type 
INT4 
OUTLIER 
Returning 
X 
Type Ref To 
ZKS_COL_KMEANS_OUTLIER_TT 
METHOD outlier.
DATA: numattributes TYPE i VALUE 0,
means TYPE REF TO zks_col_kmeans_means_tt,
centroids TYPE REF TO zks_col_kmeans_centroids_tt,
total_dist TYPE i,
rawdatalength TYPE i,
i TYPE i VALUE 1,
c TYPE i,
dist TYPE i,
wa_outlier like LINE OF outlier>*.
numattributes = lines( rawdata[ 1 ]table ).
FIELDSYMBOLS: <lr_rawdata> TYPE zks_col_kmeans_rawdata_tt.
CREATE DATA outlier.
means = allocatemeans( numclusters = numclusters
numattributes = numattributes ).
centroids = allocatecentroids( numclusters = numclusters
numattributes = numattributes ).
ASSIGN rawdata TO <lr_rawdata>.
updatemeans( rawdata = <lr_rawdata>
clustering = clustering
means = means ).
updatecentroids( rawdata = rawdata
clustering = clustering
means = means
centroids = centroids ).
rawdatalength = lines( rawdata ) + 1.
WHILE i NE rawdatalength.
c = clustering>*[ i ]id.
IF c EQ cluster.
dist = distance( tuple = rawdata[ i ]table vector =
centroids>*[ cluster + 1 ]table ).
total_dist = total_dist + dist.
ENDIF.
i = i + 1.
ENDWHILE.
total_dist = total_dist / rawDataLength.
i = 1.
WHILE i NE rawdatalength.
c = clustering>*[ i ]id.
IF c EQ cluster.
dist = distance( tuple = rawdata[ i ]table vector =
centroids>*[ cluster + 1 ]table ).
If dist > total_dist.
wa_outliertable = rawdata[ i ]table.
APPEND wa_outlier to outlier>*.
EndIf.
ENDIF.
i = i + 1.
ENDWHILE.
ENDMETHOD.

Name 
INITCLUSTERING 
Description 
Create and initialize clustering internal table with randomized values for each centroid id and assign “0” value to its data field. Each row has been assigned to a cluster id in dataset in this array 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
NUMTUPLES 
Importing 

Type 
INT4 
NUMCLUSTERS 
Importing 

Type Ref To 
INT4 
RANDOMSEED 
Importing 

Type 
INT4 
CLUSTERING 
Returning 
X 
Type 
ZKS_COL_KMEANS_CLUSTERING_TT 
METHOD initclustering.
FIELDSYMBOLS: <clustering> TYPE zks_col_kmeans_clustering_tt.
DATA: lv_random TYPE REF TO cl_abap_random_int,
lv_wa_clustering LIKE LINE OF <clustering>,
lv_counter TYPE i.
lv_random = cl_abap_random_int=>create( seed = CONV i( syuzeit )
min = 0
max = numclusters  1 ).
CREATE DATA clustering.
ASSIGN clustering>* TO <clustering>.
lv_counter = 0.
WHILE lv_counter NE numclusters.
lv_wa_clusteringid = lv_counter.
APPEND lv_wa_clustering TO <clustering>.
lv_counter = lv_counter + 1.
ENDWHILE.
WHILE lv_counter LE numtuples  1.
lv_wa_clusteringid = lv_random>get_next( ).
APPEND lv_wa_clustering TO <clustering>.
lv_counter = lv_counter + 1.
ENDWHILE.
ENDMETHOD.

Name 
ALLOCATECENTROIDS 
Description 
Create and initialize centroid value internal table 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
NUMTUPLES 
Importing 

Type 
INT4 
NUMCLUSTERS 
Importing 

Type 
INT4 
CENTROIDS 
Returning 
X 
Type Ref To 
ZKS_COL_KMEANS_CENTROIDS_TT 
METHOD allocatecentroids.
DATA: lv_wa_centroids LIKE LINE OF centroids>*,
lr_attributes TYPE REF TO zks_col_kmeans_attr_table_tt,
wa_attributes LIKE LINE OF lr_attributes>*,
lv_cluster_counter TYPE i VALUE 0,
lv_attribute_counter TYPE i VALUE 0.
CREATE DATA centroids.
WHILE lv_cluster_counter NE numclusters.
CREATE DATA lr_attributes.
WHILE lv_attribute_counter NE numattributes.
APPEND wa_attributes TO lr_attributes>*.
lv_attribute_counter = lv_attribute_counter + 1.
ENDWHILE.
MOVE lr_attributes>* TO lv_wa_centroidstable.
APPEND lv_wa_centroids TO centroids>*.
lv_cluster_counter = lv_cluster_counter + 1.
lv_attribute_counter = 0.
ENDWHILE.
ENDMETHOD.

Name 
ALLOCATEMEANS 
Description 
Create and initialize mean value internal table 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
NUMTUPLES 
Importing 

Type 
INT4 
NUMCLUSTERS 
Importing 

Type 
INT4 
MEANS 
Returning 
X 
Type Ref To 
ZKS_COL_KMEANS_MEANS_TT 
METHOD allocatemeans.
DATA: lv_wa_means LIKE LINE OF means>*,
lr_attributes TYPE REF TO zks_col_kmeans_attr_table_tt,
wa_attributes LIKE LINE OF lr_attributes>*,
lv_cluster_counter TYPE i VALUE 0,
lv_attribute_counter TYPE i VALUE 0.
CREATE DATA means.
WHILE lv_cluster_counter NE numclusters.
CREATE DATA lr_attributes.
WHILE lv_attribute_counter NE numattributes.
APPEND wa_attributes TO lr_attributes>*.
lv_attribute_counter = lv_attribute_counter + 1.
ENDWHILE.
MOVE lr_attributes>* TO lv_wa_meanstable.
APPEND lv_wa_means TO means>*.
lv_cluster_counter = lv_cluster_counter + 1.
lv_attribute_counter = 0.
ENDWHILE.
ENDMETHOD.

Name 
UPDATEMEANS 
Description 
Update mean values 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
RAWDATA 
Importing 

Type 
ZKS_COL_KMEANS_RAWDATA_TT 
CLUSTERING 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CLUSTERING_TT 
MEANS 
Importing 
X 
Type Ref To 
ZKS_COL_KMEANS_MEANS_TT 
METHOD updatemeans.
DATA: numclusters TYPE i,
wa_means LIKE LINE OF means>*,
lr_attributes TYPE REF TO zks_col_kmeans_attr_table_tt,
wa_attributes LIKE LINE OF lr_attributes>*.
FIELDSYMBOLS: <fs> TYPE zks_col_kmeans_attr_table_tt.
numclusters = lines( means>* ).
LOOP AT means>* INTO wa_means.
ASSIGN wa_meanstable TO <fs>.
LOOP AT <fs> INTO wa_attributes.
wa_attributesvalue = 0.
MODIFY <fs> FROM wa_attributes.
ENDLOOP.
MODIFY means>* FROM wa_means.
ENDLOOP.
DATA: clustercounts TYPE STANDARD TABLE OF i WITH EMPTY KEY,
countnumclusters TYPE i,
rawdatalength TYPE i VALUE 0,
i TYPE i VALUE 1, " cluster array index start with 1
j TYPE i VALUE 1, " means array index start with 1
k TYPE i VALUE 1, " means array index start with 1
l TYPE i VALUE 1, " means array index start with 1
cluster TYPE i,
meanslength TYPE i VALUE 0,
meansattributeslength type i value 0,
rawdataattributeslength type i value 0.
WHILE numclusters NE countnumclusters.
APPEND 0 TO clustercounts.
countnumclusters = countnumclusters + 1.
ENDWHILE.
rawdatalength = lines( rawdata ) + 1.
WHILE i NE rawdatalength.
cluster = clustering>*[ i ]id + 1.
clustercounts[ cluster ] = clustercounts[ cluster ] + 1.
j = 1.
rawdataattributeslength = lines( rawdata[ i ]table ) + 1.
WHILE j NE rawdataattributeslength.
means>*[ cluster ]table[ j ]value =
means>*[ cluster ]table[ j ]value +
rawdata[ i ]table[ j ]value.
j = j + 1.
ENDWHILE.
i = i + 1.
ENDWHILE.
meanslength = lines( means>* ) + 1.
WHILE k NE meanslength.
meansattributeslength = lines( means>*[ k ]table ) + 1.
l = 1.
WHILE l NE meansattributeslength.
means>*[ k ]table[ l ]value =
means>*[ k ]table[ l ]value / clustercounts[ k ].
l = l + 1.
ENDWHILE.
k = k + 1.
ENDWHILE.
ENDMETHOD.

Name 
UPDATECENTROIDS 
Description 
Update centroid values 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
RAWDATA 
Importing 

Type 
ZKS_COL_KMEANS_RAWDATA_TT 
CLUSTERING 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CLUSTERING_TT 
MEANS 
Importing 

Type Ref To 
ZKS_COL_KMEANS_MEANS_TT 
CENTROIDS 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CENTROIDS_TT 
METHOD updatecentroids.
DATA: centroidslenght TYPE i VALUE 0,
k TYPE i VALUE 1, " means array index start with 1
centroid TYPE REF TO zks_col_kmeans_centroids_tt.
centroidslenght = lines( centroids>* ) + 1.
WHILE k NE centroidslenght.
centroid = computecentroid( rawdata = rawdata
clustering = clustering
cluster = k  1
means = means ).
centroids>*[ k ]table = centroid>*[ 1 ]table.
k = k + 1.
ENDWHILE.
ENDMETHOD.

Name 
ASSIGN 
Description 
Update centroid values 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
RAWDATA 
Importing 

Type 
ZKS_COL_KMEANS_RAWDATA_TT 
CLUSTERING 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CLUSTERING_TT 
CENTROIDS 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CENTROIDS_TT 
CHANGED 
Returning 
X 
Type 
ABAP_BOOL 
METHOD assign.
DATA: numclusters TYPE i VALUE 0,
distances TYPE zks_col_kmeans_distances_tt,
wa_distances LIKE LINE OF distances,
distancescounter TYPE i VALUE 0,
rawdatalength TYPE i VALUE 0,
i TYPE i VALUE 1,
k TYPE i VALUE 1,
newcluster TYPE i VALUE 0.
numclusters = lines( centroids>* ).
WHILE distancescounter NE numclusters.
APPEND wa_distances TO distances.
distancescounter = distancescounter + 1.
ENDWHILE.
rawdatalength = lines( rawdata ) + 1.
WHILE i NE rawdatalength.
k = 1.
WHILE k NE numclusters + 1.
distances[ k ] = distance( tuple = rawdata[ i ]table
vector = centroids>*[ k ]table ).
k = k + 1.
ENDWHILE.
newcluster = minindex( distances = distances ).
IF newcluster <> clustering>*[ i ]id.
changed = abap_true.
clustering>*[ i ]id = newcluster.
ENDIF. " else no change
i = i + 1.
ENDWHILE.
ENDMETHOD.

Name 
DISTANCE 
Description 
Measure distance from data to centroid by using Euclid calculation 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
TUPLE 
Importing 

Type 
ZKS_COL_KMEANS_ATTR_TABLE_TT 
VECTOR 
Importing 

Type 
ZKS_COL_KMEANS_ATTR_TABLE_TT 
RETVAL 
Returning 

Type 
INT4 
METHOD distance.
DATA: sumsquareddiffs TYPE i VALUE 0,
tuplelength TYPE i,
j TYPE i VALUE 1,
power TYPE i VALUE 0.
tuplelength = lines( tuple ) + 1.
WHILE j NE tuplelength.
power = ipow( base = tuple[ j ]value  vector[ j ]value exp = 2 ).
sumsquareddiffs = sumsquareddiffs + power.
j = j + 1.
ENDWHILE.
retval = sqrt( sumsquareddiffs ).
ENDMETHOD.

Name 
MININDEX 
Description 
Cluster id of the cluster that has centroid closest to the tuple 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
DISTANCES 
Importing 

Type 
ZKS_COL_KMEANS_DISTANCES_TT 
NEWCLUSTER 
Returning 

Type 
INT4 
METHOD minindex.
DATA: indexofmin TYPE i VALUE 0,
smalldist TYPE i VALUE 0,
k TYPE i VALUE 1,
distanceslength TYPE i VALUE 0.
distanceslength = lines( distances ) + 1.
smalldist = distances[ 1 ].
WHILE k NE distanceslength.
IF distances[ k ] < smalldist.
smalldist = distances[ k ].
indexofmin = k  1.
ENDIF.
k = k + 1.
ENDWHILE.
newcluster = indexofmin.
ENDMETHOD.

Name 
COMPUTECENTROID 
Description 
Determine centroid values 
Level 
Instance Method 
Visibility 
Private 
Parameter 
Type 
Pass By Value 
Typing Method 
Associated Type 
RAWDATA 
Importing 

Type 
ZKS_COL_KMEANS_RAWDATA_TT 
CLUSTERING 
Importing 

Type Ref To 
ZKS_COL_KMEANS_CLUSTERING_TT 
CLUSTER 
Importing 

Type 
INT4 
MEANS 
Importing 

Type Ref To 
ZKS_COL_KMEANS_MEANS_TT 
CENTROID 
Importing 
X 
Type Ref To 
ZKS_COL_KMEANS_CENTROIDS_TT 
METHOD computecentroid.
DATA: numattributeslength TYPE i,
rawdatalength TYPE i,
i TYPE i VALUE 1,
j TYPE i VALUE 1,
mindist TYPE f VALUE '1.7976931348623157E+308',
attributecounter TYPE i,
wa_centroid LIKE LINE OF centroid>*,
centroidlength TYPE i,
attributes TYPE REF TO zks_col_kmeans_attr_table_tt,
wa_attributes LIKE LINE OF attributes>*.
numattributeslength = lines( means>*[ 1 ]table ).
rawdatalength = lines( rawdata ) + 1.
CREATE DATA attributes.
WHILE attributecounter NE numattributeslength.
APPEND wa_attributes TO attributes>*.
attributecounter = attributecounter + 1.
ENDWHILE.
CREATE DATA centroid.
wa_centroidtable = attributes>*.
APPEND wa_centroid TO centroid>*.
WHILE i NE rawdatalength.
DATA: c TYPE i.
c = clustering>*[ i ]id.
IF c EQ cluster.
DATA currdist TYPE f.
currdist = distance( tuple = rawdata[ i ]table
vector = means>*[ cluster + 1 ]table ).
IF currdist < mindist.
mindist = currdist.
centroidlength = lines( centroid>*[ 1 ]table ) + 1.
j = 1.
WHILE j NE centroidlength.
centroid>*[ 1 ]table[ j ]value =
rawdata[ i ]table[ j ]value.
j = j + 1.
ENDWHILE.
ENDIF.
ENDIF.
i = i + 1.
ENDWHILE.
ENDMETHOD.

Asymptotic Time Complexity:
Please find asymptotic time complexity analysis results of methods in KMeans class, below;
Method 
Value 
CLUSTER 
n^2 
INITCLUSTERING 
n 
ALLOCATECENTROIDS 
n^2 
ALLOCATEMEANS 
n^2 
UPDATEMEANS 
n^2 
UPDATECENTROIDS 
n 
ASSIGN 
n^2 
OUTLIER 
n^2 
DISTANCE 
n 
MININDEX 
n 
COMPUTECENTROID 
n^2 
Summary:
This is an ABAP implementation of kMeans algorithm. Of course this code can be written more optimized way. Or may add and remove or merge with some other functionalities regarding problem needs.
Please find transport request files under Github. Import transport request into your sandbox or development system.
Reference:
Data Clustering – Detecting Abnormal Data Using kMeans Clustering
About Author:
Please find my personal bio here;
I have been working at KoçSistem as a SAP basis technology executive team leader and principal SAP technical architect, since 2013. With my 26 years experience on ABAP development and 21 years experience on SAP basis fields, during my consulting engagements I worked on various technologies such as cloud, robotic programming, ML, mobile application development, application server programming and many fields and platforms. Currently studying at Gazi University MSc. Computer Science department, since 2021.
SAP Community Network topic leader @
 2011/2012 – Databases and OS Platforms
 2012/2013 – SAP On Oracle
Hi Orkun Gedik!
I think this is somehow really cool. Well written and structured.
Just to understand your intension. As you have written on HANA systems PAL is available and you should be able to use PAL here e. g. via AMDP. Why writing it in ABAP as ABAP on Any DB becomes increasingly lesser relevant?
Hi Peter Baumann
First of all I am very appreciated by your comments. Thank you so much.
I was expecting such comment and you are first one There are many intentions I have;
I hope that it is clear. Thank you again.
Orkun Gedik
You are right, license is sometimes a topic. Typically as you are on the application layer and if you stay at the application layer it should be safe. But always als your SAP sales contact first 🙂
Good intensions, thank you for your contribution!
Peter
Maybe interesting regarding your points 2 and 3.
Typically every SAP NetWeaverbased ABAP system should include a  even often not used  BW system. BW includes a Data Mining workbench (Tx RSDMWB) and an Analysisprocess Designer (Tx RSANWB). This includes some typical classical algorithms as KMeans for Clustering.
If interested, just check if your system includes function group RS_DME_CLUSTERING_KMEANS or if transactions mentioned are working.
As this modeling is InfoObjectsbased it is maybe not so easy to be handled by nonBW guys but maybe it is interesting so see for developers how it is implemented by SAP.
Sure. Very good approach. But, it is just a kMeans algorithm implementation. RS_DME_CLUSTERING_KMEANS do not have outliers. But it can be enhanced.
Best regards,
Orkun Gedik
hi Orkun Gedik great article and effort first of all. I used PAL library for many scenarios and it comes with HANA runtime license. You need to install and use straight away. Only limitation here is, if you want to implement your own PAL procedures(with some tools), it is only allowed in developer license. For nonhana environments i think above is the easiest solutions. I even did a POC to deploy a docker image to SAP cloud foundry for some statistical calculations via Python :D.
Of course anything in ABAP would be better as long as performance is not a concern.
Cool! Have you considered using abapGit and publishing the code, for example, also on GitHub?
Thank you Andrea Borgia
Very good idea! I will do that.
Best regards,
Orkun Gedik
You're welcome. That way people can download the whole code, structures, test code, everything, it's developer nirvana basically 😀
Hello Orkun,
thanks a lot for taking the time to share.
Note: I am assuming this definition is creating a CHAR and an INTEGER, while you want two integers.
best regards,
JNN
Hello Jacques Nomssi Nzali,
You are correct. I missed this point and fixed it regarding your warning.
Thank you and best regards,
Orkun Gedik
Hello Orkun,
can you please add the definition of the KMEANS_CLUSTERING structure?
I am trying to test with the data from your reference
but I have not figured out yet whether my code is correct. I think an output like in the demo would be useful. Could you please share the expected results for unit testing?
best regards,
JNN
Hi Jacques Nomssi Nzali,
Your code is looking correct. Regarding values you provided on your sample; please find expected outliers test result with 3 clusters and 30 repeat, below. You can optimize outliers method or modify as your needs.
You can add as many fields as you need into your dataset as you made. Additionally you can import transport requests on Github link provided over the article under summary header. So you can get away from creating whole structures, including class and sample program.
Please feel free to ask, if you have further questions. Thank you.
Best regards,
Orkun Gedik
Thank you Orkun, I got it to work now.
I am probably missing something, but your outlier computation of a mean distance
seems incorrect to me. Shouldn't it be
With ClusterDataLength beeing the number of data in the cluster?
best regards,
JNN
Hi Nomssi,
total_dist = total_dist / rawDataLength is correct. This because rawDataLength stores total number of elements of whole tuples. For example in your example, its value is 20.
But you can modify and apply your solution regarding your needs as I mentioned.
Best regards,
Orkun Gedik