Ensemble Clustering on different data transformations

[Kosisochi]

From the documentation, i can only see capabilities for clustering ensemble for different k or different algorithm algorithms and so on.
What I want to do is to cluster different "views" of the same data. Example: Bag of Words , TFIDF and word embedding representation of the same data as an ensemble.

I have tried creating different data objects of the different data representation.

dataObj = oe.data(df2, list_x)
dataObj1 = oe.data(df3, list_xj)

dataObj.D["parent1"] = dataObj1
c = oe.cluster(dataObj)
K = 5
numIterations = 2
c_MV_arr = []
source_names = ['parent', 'parent1']
output_names = ["BOW", "TFIDF"]
for i in range(1,numIterations):
    name = 'kmeans_' + output_names[i]  
    c.cluster(source_names[i], 'kmeans', name, K, init = 'random', n_init = 1) 
    c_MV_arr.append(c.finish_majority_vote(threshold=0.5))

but i get this error

TypeError: float() argument must be a string or a number, not 'data'

I also tried multiple c's

c = oe.cluster(dataObj)
c1 = oe.cluster(dataObj1)

but i cant calculate c.finish_majority_vote(threshold=0.5) as it only takes c into consideration and not c1

Is it possible to use OpenEnsembles to cluster ensembles of different data?

Note: The feature dimension of df2 and df3 are different and so cannot do the transformation into BOW features or TFIDF features inside the for loop because the length of the features (length of df2.colums or df3.colums) is the second argument for oe.data(df,x) and this is initialized outside the for loop.

[Kosisochi]

To answer my question, this is what i did:
I added the new data object to a list d_arr and the used the .merge() method to add the new data object to the first data object

numRepeats = 1
d_arr=[]
for i in range(0,numRepeats):
    d_temp = oe.data(df3, list_xj)
    d_arr.append(d_temp)

transdict= dataObj.merge(d_arr)

Then i initialized a cluster object using the first data object

c = oe.cluster(dataObj)
for name in dataObj.D.keys():
       -----------------------------------
         c.cluster(name, "kmeans", "kmeans_"+ name + "_" , K , n_init = 1)

Where name refers to a different data object. The first data object is caled "parent" and a subsequent on is called "parent_1" and so on