Unsupervised Learning - Science topic

We can be Validating the quality of clusters created by clustering algorithms is an important step in unsupervised learning. There are several methods you can use to evaluate the performance of clustering algorithms such as K-means. Here are a few commonly used techniques:

There are several algorithms that are commonly used for semi-supervised and unsupervised learning in medical imaging. Some of the most powerful algorithms include:

It's worth noting that the effectiveness of these algorithms can vary depending on the specific task and dataset being used, and it's important to carefully evaluate their performance before deploying them in a clinical setting.

Dear Edith Ohri ,

What is unsupervised learning? Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention.

https://www.ibm.com/topics/unsupervised-learning#:~:text=the%20next%20step-,What%20is%20unsupervised%20learning%3F,the%20need%20for%20human%20intervention.

Regards,

Shafagat

K-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm.

U can do conversion of unsupervised to supervised at initial phase of prediction process.

Thank you for taking the time to reply Muhammad Ali .

I looked at the linked resources but do not see anything related to my question (*nested categorical* variables, not simple categorical variables). In case I missed it, could you please indicate the relevant section?

Kind regards,

Mick

why not consider using S-transform as it combines the properties of FFT and wavelet transform.

Dear Dr Fatemeh Daneshfar . See the following useful RG link:

Dear Amin,

This is a good question. The below link describes very well the solid definition of transfer learning and mentioned why and when we should use a pre-trained model

"A pre-trained model may not be 100% accurate in your application, but it saves huge efforts required to re-invent the wheel."

Dear Cecilia,

Hope these links can help you!

Dear Saad,

Have a look at the following articles

Hope it will help you.

Dear Md Shakil Mahabub

These papers might be an asset, have a look:

1. https://www.mdpi.com/journal/geosciences/special_issues/applications_artificial_intelligence

2. https://www.researchgate.net/publication/349365459_Application_of_deep_learning_algorithms_in_geotechnical_engineering_a_short_critical_review

3.

4. https://www.sciencedirect.com/science/article/pii/S1674987115000821

5. https://www.intechopen.com/online-first/76633

6. https://tu-freiberg.de/sites/default/files/media/professur-felsmechanik-32204/E-book/47_artificial_intelligence_in_geotechnical_engineering_0.pdf

Kind Regards

Dear Srinivas:

For classification and regression tasks, you can train various types of neural networks using the trainNetwork function.

i.e. you can train:

-- a convolutional neural network (ConvNet, CNN) for image data.

-- a recurrent neural network (RNN) such as a long short-term memory (LSTM) or a gated recurrent unit (GRU) network for sequence and time-series data

-- a multilayer perceptron (MLP) network for numeric feature data.

You can train on either a CPU or a GPU. For image classification and image regression, you can train a single network in parallel using multiple GPUs or a local or remote parallel pool. Training on a GPU or in parallel requires Parallel Computing Toolbox™. To use a GPU for deep learning, you must also have a supported GPU device. For information on supported devices, see GPU Support by Release (Parallel Computing Toolbox). To specify training options, including options for the execution environment, use the trainingOptions function.

When training a neural network, you can specify the predictors and responses as a single input or in two separate inputs.

Thus, the entirety of this process depends mainly on the properties of these two hardware (cpu or Gpu).

I hope it will be helpful..

With my best wishes ...

Did you mean to type pre-test?

Tazim Ameen Thanks

Interesting

Clustering techniques are commonly employed for localization within wireless networks based on unsupervised learning. Wang et al. 14 proposed UnLoc, an unsupervised learning method for indoor localization

Maybe label group result to know what am i going to mesaure

hi, of course you can do feature selection in unsupervised learning, but you must do it if model accuracy is not acceptable. You can use correlation analysis, t-statistics, PCA etc. Also it is possible for unsupervised classification in EEG without feature extraction if model accuracy is acceptable else you need to get more relevant features. Those all are based on experiments. Don't fear, try it.

Your question has sparked some interest with me because it has some correlation with the research I'm currently chasing. One of the ten questions concerning the heart is as Follows: Heart, 5. The pacemaker of the heart is characterized by? Answer = SA Node. So when I dissect your question the word node correlates to my research race which then involves a pacemaker for my answer, not necessarily for yours, but maybe we could work together to help each other. That's the one part of your question, the other three are DBN network, HLN and RBM, so for the question is How many Hearts are there in a Deep Belief Network, for you it would be, Is there a heart in a Deep Belief Network, does a RBM machine have a heart, which would correlate with my current studies in Data Science, especially on the topic does Artificial Intelligence exist, I.e could a machine have a heart, yes a physical one mechanical but could it have a heart, like Socrates 6 questions, what is virtue, what is moderation, what is piety, what is good, what is justice, my answer is Heart, not a physical one an emotional one, could AI fall in love and find mercy on a human being. Could AI deliberately kill a human being out of jealousy, because all it does is work for humans, could AI feel like a slave and try to overthrow the human government, these are all the questions for my research and thesis, but coming back to your question, how could we know what's hidden, would it not be infinite, if we have no parameters, just like space, just like multiple dimensions, just like time divided into, micro micro nano nano seconds all the way to infinity. My only assistance I can give you to your question is, How many True hearts does your Deep Belief Network have and does your RBM machine have AI technology to perhaps hide the information from you, if the answer is No, then there are no Hidden Layers, there is no Artificial Intelligence, and your machine does not have a heart like my definition not a emotional

one, a physical one. Not deviating from your question, there are nodes or main cities in the heart and each node has a spark, so the number of sparks would create a network if connected together or sparks with wi-fi, then there would be no road map, just main nodes transferring energy throughout the system with no downtime, no waiting time for downloads, instantaneous answers in split seconds, no buffering...etc etc etc. Then there would be no hidden numbers, every dimension would be recorded, and a number specified, you would not even get lost, you would immediately be at the right place at the right time. That would be my Deep Belief Network, I would be 100% sure, I would know just like my online company motto, Knowing, https://pvg-investments.business.site. This is my mission to know everything 100% no doubt, and I know that if I use technology correctly I can achieve this, with your help, I know I did not answer your question, but I hope my questions help you answer your own question.

You may try this algorithm:

B. K. Tripathy and D. Mittal: Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis, Applied soft computing, 46, (2016), pp.886-923.

IMO this is the best book available on Clustering: Finding Groups in Data: An Introduction to Cluster Analysis | Leonard Kaufman, Peter J. Rousseeuw | download (b-ok.cc) The R programs are available here: https://www.google.com/search?client=firefox-b-1-d&q=R+package+for+Finding+Groups+in+Data

Best wishes, David Booth

So you need to code your network to get the input of size (none, 1, 48). Each feature is of dimension 1x48, while 'none' would take up the size of the number of sample points (335 in your case). Hence, your input would be of shape 335x1x48. So, modify the input layer of your network to expect input of size 1x48 instead of 335x48, and provide input as 335x1x48.

Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pre-trained models, and apps. You can build network architectures with the Deep Network Designer app, you can design, analyze, and train networks graphically. The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. You can visualize layer activations and graphically monitor training progress.

You can take help from the internet and follow the link for making his own architecture accordingly to your input.

Neural network is considered as one of the most topic in text classification

the attached file is the newest article review

This article

Kevin Hartnett, "A Mathematical Model Unlocks the Secrets of Vision", Quanta Magazine August 21, 2019

discusses a mathematical model of "the visual cortex as a swirling play of feedback loop upon feedback loop".

The basic premise of transfer learning is simple: take a model trained on a large dataset and transfer its knowledge to a smaller dataset. For object recognition with a CNN, we freeze the early convolutional layers of the network and only train the last few layers which make a prediction.

You can use MobileBERT, it's a compact BERT model open sourced on GitHub. ... or the other Google open source models using projection methods, namely SGNN, PRADO and pQRNN. pQRNN is much smaller than BERT, but is quantized, and can nearly achieve BERT-level performance, despite being 300x smaller and being trained on only supervised data.

Have a look at ::

https://medium.com/@ODSC/assessment-metrics-for-clustering-algorithms-4a902e00d92d

See also

https://stats.stackexchange.com/questions/6252/clustering-quality-measure

If you know (or any rough idea) how many clusters you need, then you may try K-means or some other variants of it like K-means++ or MinMax K-means clustering algorithm. But if you do not have prior knowledge about number of clusters then you can try with different values of k, and assess the goodness of the results using some cluster validity indices like DB index, C index, CH index, Dunn index etc. Otherwise you can DBSCAN clustering algorithm.

In my experience in suggest for this problem to use supervised by using the Artificial Neural Networks, we can you find different architecture have the background or inspiration from comportment of human, or example NN, CNN, RNN... etc.

I hope that be Claire for you. @ Issa Annamoradnejad

If you kindly read the attached book deeply, I hope you will be able get very good concepts of supervised (Learning via labeled data) and unsupervised learning (Learning via non-labeled data). Also you would need some examples preferably via MATLAB to see how it work? : You may follow some example from here: https://github.com/hrzafer/machine-learning-class and https://www.coursera.org/learn/machine-learning

Multi-layer networks can't and don't alleviate the problems caused by one-to-many mappings. What they do is allow the representation of associations that aren't linearly separable.

Since the output of any unit of an intermediate layer becomes an input to the next layer, the range of input/output values is determined by the activation function of each unit. So it's simple to limit the range-just specify that the activation function.

There is a long discussion about the work (i.e., paper) reported in the GitHub link you provided, it concerns the paper's rejection at a conference:

I think the statement "human and animal learning are unsupervised" is not exactly correct, we do need to labeled data during our learning. Like you constantly correct your children when they learn at a young age. The limitation of unsupervised learning is not just labeled data, also the difficulty to convert abstract tasks in a machine-readable format.

Currently, there are some progressed, such self-supervised, using the property of data as pseudo-labels, Semi-supervised learning, etc.

Samy A. Azer Thank you , i understand the confusion in the question statement, between AI process or general process. Sorry.

Hello,

I think the meaning of "good clustering" is subjective and its interpretation varies with applications. The notion of "good clustering" is relative, and it is a question of point of view.

However, there are few well known measures like silhouette width (SW), the Davies-Bouldin index (DB), the Calinski-Harabasz index (CH), the Normalized Mutual Information(NMI) and the Dunn index.

In my point of view, I think that the single-link metric is flexible in the sense that it can find non-isotropic clusters and also the clusters can be even concentric. The single-link metric works best for well-separated, non-spherical clusters.

Good luck.

Dear Mohsen,

Have a look at http://www.mit.edu/~9.54/fall14/slides/Class13.pdf

European Scientists have identified 31 medications that may help for SARS-2-Treatment, see https://www.pharmazeutische-zeitung.de/31-wirkstoffe-haben-potenzial-gegen-covid-19/

Anton N. Gvozdetsky , Shafagat Mahmudova , Pratyay Sengupta thank you all so much!

Regards,

Antonio

I always concerns why we need deep learning on RDF or OWL-based data. As the natural of the semantic web, the graph data with specific relations actually presents the outcome of learning. Thus, the query language or rule query language should enable directly answer the questions.

Or, also a posdoc can be recomendable

All are approaches that exploits the computational intelligence paradigm. Machine learning is refered to data analitics. Evolutionary computation deal with optimization problems.

I think you should analysis PCAP file and converting each package to record before using the data with machine learning algorithms. I suggest to read the KDD Cup 99 Intrusion Detection dataset for understanding the information of each package.

In addition to what was recommended above, I suggest that you should first think how better create data vectors. As I understand, you have categorical data. For example, "user_job title" is a list of professions expressed by words, not numbers. So, instead of using N digits to represent these professions in a vector you can use a binary scheme called "one-hot encoding".

Concerning the number of clusters, it is actually an important issue that shows what result you want to get and what exactly you want to know about your data. You can ask experts, how many categories they expect to see as the result, or to create a scale of "skill and work experience" and divide it into segments. Or you can ask experts to label a small group of users and use the labeled data as a standard. You can also try to analyze your data mathematically or statistically, however, the method will again depend on what you want to know about your data in the end.

PCA will result in slower processing compared with an Autoencoder and note that a non-linear AE will be non-linear except when the input data is spanned linearly.

My papers (about HiDoclus and OhDoclus) and the thesis proposal are available on my profile page with full text.

If you have any problem please let me know and I can send them to you.

Rui Encarnação

You can take a look here as well :

https://www.youtube.com/watch?v=h7iBpEHGVNc&t=3574s

a very interesting lecture.

Hi dear,

Please check it up:

Good Luck.

Hi Aniruddha,

If you are able to spend some money on acquiring the knowledge, then Udacity's Self Driving Course is one of the best places to get started. More info at https://in.udacity.com/course/self-driving-car-engineer-nanodegree--nd013

The best part is they have open sourced some part of the codes which can be a great starting point. The codes are available at https://github.com/udacity/self-driving-car

To write software for self driving cars, I would recommend using ROS (http://www.ros.org/). ROS have many inbuilt functionalities like object detection, path planning, node controls etc which can get you started easily. ROS Wiki (https://wiki.ros.org/) can offer you a glimpse of what ROS is capable of.

ROS turtlebot autonomous navigation (https://wiki.ros.org/turtlebot_navigation/Tutorials/Autonomously%20navigate%20in%20a%20known%20map) will be a great tutorial to start with.

Though I have never used, https://www.duckietown.org/independent/guide-for-learners is also an interesting platform to start with.

Regards,

Vishnu Raj

PS: If you find this answer useful, don't forget to upvote.

For supervised learning we need to have a labeled data set. If not, it is good to run unsupervised learning algorithms for automatically labeling unlabeled data. Once the data is labelled using clustering algorithms, then it is possible to use supervised learning algorithms. For linking the two tasks a simple script can be written that connect the output of clustering as an input for the classification task.

Dear M. Awad,

The purpose of my previous answer was just for providing a little example. Obviously, the random training samples of my previous answer have to substituted by the actual training samples of the corresponding problem.

Kind regards,

Carlos.

I made a youtube video for this. See https://www.youtube.com/watch?v=Db9Bnss8b-8&t=68s

Thanks

Anselm

Dear Blust,

Please follow the papers given below:

1. Bialkowski, A., Lucey, P., Carr, P., Yue, Y., Sridharan, S., & Matthews, I. (2014, December). Large-scale analysis of soccer matches using spatiotemporal tracking data. In Data Mining (ICDM), 2014 IEEE International Conference on (pp. 725-730). IEEE.

2. Link, D. (2018). Data Analytics in Professional Soccer: Performance Analysis Based on Spatiotemporal Tracking Data. Springer.

3. https://pdfs.semanticscholar.org/06aa/87ce028562aee47b05a6dc26bb926e346ffc.pdf

See German Credit fraud data at

You can used clustering algorithm like

1. K-means/medoid

2. Fuzzy c-means

3. partition-based clustering etc. for the classification issue.

The basic idea is that semantic vectors (such as the ones provided by Word2Vec) should preserve most of the relevant information about a text while having relatively low dimensionality which allows better machine learning treatment than straight one-hot encoding of words. Another advantage of topic models is that they are unsupervised so they can help when labaled data is scarce. Say you only have one thousand manually classified blog posts but a million unlabeled ones. A high quality topic model can be trained on the full set of one million. If you can use topic modeling-derived features in your classification, you will be benefitting from your entire collection of texts, not just the labeled ones.

Getting the embedding

Ok, word embeddings are awesome, how do we use them? Before we do anything we need to get the vectors. We can download one of the great pre-trained models from GloVe:

The (python) meat

We got ourselves a dictionary mapping word -> 100-dimensional vector. Now we can use it to build features. The simplest way to do that is by averaging word vectors for all words in a text. We will build a sklearn-compatible transformer that is initialised with a word -> vector dictionary.

These vectorizers can now be used almost the same way as CountVectorizer or TfidfVectorizer from sklearn.feature_extraction.text. Almost - because sklearn vectorizers can also do their own tokenization - a feature which we won’t be using anyway because the benchmarks we will be using come already tokenized. In a real application I wouldn’t trust sklearn with tokenization anyway - rather let spaCy do it.

Now we are ready to define the actual models that will take tokenised text, vectorize and learn to classify the vectors with something fancy like Extra Trees. sklearn’s Pipeline is perfect for this:

Sharing link:

The state-of-the-art for most text classification applications relies on embedding your text in real-valued vectors:

The gensim package is popular for training word vectors on data: https://radimrehurek.com/gensim/models/word2vec.html

This method relies on having rich, diverse collections of words and contexts, which your data may not have on its own. Thus it's popular to initialize your embedding matrix using pre-trained word vectors like word2vec or fasttext; in some cases, these will work out of the box, in some you'll want to continue training the vectors on your dataset, in others it's better to just train on your data alone.

The great thing about embedding methods is they don't care about language; you can create an embedding for any language or really any sequential data that endows discrete data with a sort of 'meaning'.

Once you have richer features from your embedding matrix, you can use these as inputs to a classifier, which can be as simple as softmax regression, which assigns probabilities to discrete classes, or as complex as an RNN/LSTM, which ultimately can do the same but typically for sequential data.

The choices you make here depend more heavily on what specific problem you're trying to solve, but here are a few examples:

Pekka already answered your question to me.

Naturally both your development data and validation data require pre-processing. What I mean is that you should under no circumstance pool both data sets and pre-process it as a whole for classification. Take for instance normalisation as a pre-processing step. Development data is used to determine scale and shift parameters. Both the development and validation data are then normalised using these parameters. If we were to use the entire combined data set for normalisation, we would bias our classification models. The other alternative (a separate normalisation based on the validation set) is not as problematic, but may decrease classification performance if the validation set is small. This does not only apply to normalisation, but to other pre-processing steps (imputation, PCA, clustering etc.) as well.

Thus, you may perform one analysis on the combined data set, but the information generated should generally not be used for building models. In your case, you may perform a segmentation study on the whole data set, and also perform classification if, and only if, segmentation is not population-based (i.e. no information about other data is used when segmenting one data set), or if the segmentation results are not used for classification.

Hi,

Prior work in signal processing and machine learning for time series problems (including the accelerometer signals) has strongly emphasised on regularly sampled and equal length time series signals, resulting in fewer methods that exist specifically for analysing irregularly sampled and particularly, extremely very few methods for unequal in length signals.

Irregularly sampled time signals: For analysing the irregularly sampled time series directly, many techniques have already been proposed for example spectral analysis [3, 4] and kernel-based methods [5]. These techniques have already been used for extracting causal structures, statistics from data in fields such as from astronomy [6, 7], palaeontology [5] and economics [8]. On the other hand irregular time series data are transformed into regularly spaced data through some form of interpolation. For supervised learning tasks such as classification tasks, this has the added advantage of enabling the time series to be equalised to a given length by sampling from the interpolated function, thereby enabling standard classification algorithms to be used.

Unequal length time signals: To tackle the problem of unequal length time series problem, we recently proposed a new machine learning regression algorithm known as the probabilistic broken-stick model [1].  Using a set of locally linear line segments, our  novel algorithm can model any complex, non-linear function catering  for both short term interpretability and long term flexibility of any any irregularly sampled/ unequal in length time series simultaneously. This article is now freely available for over a month (until 1st Jan 2018) and if you are interested, please find it here https://lnkd.in/e3-Wd6Y . This paper is also available on arxiv: https://arxiv.org/pdf/1612.01409.pdf

In an another paper [2], we proposed Gaussian process regression to make the unequal length time series equal in length for the supervised learning i.e., classification which is published in Machine learning for signal processing. The paper is also available on arxiv: https://arxiv.org/pdf/1605.05142.pdf

From references [1,2], you may find citations to other researchers' works that you may find interesting.

All the best

Santosh

[1] Norman Poh, Santosh Tirunagari, Nicholas Cole, Simon de Lusignan, Probabilistic broken-stick model: A regression algorithm for irregularly sampled and unequal length data with application to eGFR, In Journal of Biomedical Informatics, Volume 76, 2017, Pages 69-77, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2017.10.006.

[2] Tirunagari, Santosh, Simon Bull, and Norman Poh. "Automatic classification of irregularly sampled time series with unequal lengths: A case study on estimated glomerular filtration rate." In Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on, pp. 1-6. IEEE, 2016.

[3] Michael Schulz and Karl Stattegger, “Spectrum: Spectral analysis of unevenly spaced paleoclimatic time series,” Computers & Geosciences, vol. 23, no. 9, pp. 929–945, 1997.

[4] Petre Stoica, Prabhu Babu, and Jian Li, “New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data,” Signal Processing, IEEE Transactions on, vol. 59, no. 1, pp. 35–47, 2011.

[5] Kira Rehfeld, Norbert Marwan, Jobst Heitzig, and Jurgen ¨ Kurths, “Comparison of correlation analysis techniques for irregularly sampled time series,” Nonlinear Processes in Geophysics, vol. 18, no. 3, pp. 389–404, 2011.

[6] Piet Broersen, “Time series models for spectral analysis of irregular data far beyond the mean data rate,” Measurement Science and Technology, vol. 19, no. 1, pp. 14, 2008.

[7] C. Thiebaut and S. Roques, “Time-scale and time-frequency analyses of irregularly sampled astronomical time series,” EURASIP J. Appl. Signal Process., vol. 2005, pp. 2486–2499, 2005.

[8] Ulrich Muller, “Specially weighted moving averages with re- ¨ peated application of the ema operator,” 2000.

There are various index measures are available in the literature for evaluating the cluster and also go through the book of Prof. A. k. Jain regarding the cluster validity.

I think this questions have two aspects to correctly answer. First, the detection rate is higher in the supervised learning than unspervised. Second, the unspervised can easily detect know and unkown attacks and this will be better than the supervised one. You, ultimatel, can apply ensemble deep learning to improve your scheme of detecting malware.

hey osam,

in unsupervised learning, you do not have a supervisor which tells you what is right and what is wrong i.e. you do not have input data with example output data where you want to learn the input/output relation.

normally, you use unsupervised learning when you want to find general structures in your data. for example major trends, a low-dimensional embedding or clustering.

do you have a particular problem or can you refine your question?

best,

christoph

 thanks for your ideas. I will try these suggestions. once again thanks all

.

MCA on binary data will lead into a continuous factorial space (admittedly with a much smaller dimensionality if strong correlations are present)

one advantage of AR is that the results can directly be interpreted in the description space ; this is lost after MCA

however, MCA has been used together with AR so as to rank/select the "best" rules :

.

Hi Sepideh,

As you mentioned previously, they belong to different Machine Learning concepts. K-means is utilized when we don't know the labels of our training samples (Unsupervised Learning), whereas SVMs are used for Supervised Learning, in which we know the class that each training sample belongs to.

Having said that, and as Samer Sarsam mentioned, you can in some way convert the clustering problem to a classification one. One method can be the following:

After running the K-means algorithm, we are going to have each of the training samples assigned to a specific cluster. Then, what we can do is to assign or "classify" the centroid of each cluster to the class that is most "voted" for the members of the cluster.

Let's see this concept with an example. Let's assume that we have 100 training examples, 4 clusters (C1, C2, C3 and C4) and we know the labels of each of the 100 training examples (say class 1 or 2). After running the 4-means algorithm we arrive at the following configuration: C1 has assigned 20 samples, C2 another 20 samples, C3 has assigned 30 samples and C4 another 30 samples:

                   class1     class2

Now, we can assign C1 and C2 to class 1 and C3 and C4 to class 2. At test time, when a new sample arrives, we can classify the test sample to the class defined by the closest centroid.

A similar concept can be reviewed when using Self-Organizing Networks (SOM) for classification purposes.

Regards,

Carlos.

I think class distribution is not an issue  may vary depending on nature of classifier. Once keep in mind, when you prepare development and validation set try to keep class distribution uniformity. So do some stratification  before modeling.

Although PCA is not a clustering method, it can help to reveal clusters and it’s quite good for reducing dimensionality as a feature extractor and to also visualize clusters. You can run a classifier directly on your data and record the performance. But in case you are not satisfied; try PCA by selecting the number of components at the tip of sorted eigenvalue plot. Then, run the K-means. If it produces good clusters, then PCA and classifier could do the magic.

The amount of clusters is determined by 'elbow' approach according to the value within groups sum of squares. Basically, you repeat K-means algorithm for different amount of clusters and calculate this sum of squares. If the number of clusters equal to the number of data points, then sum of squares equal 0.

Also check this link: https://homes.cs.washington.edu/~ruzzo/papers/pca-bioinf.pdf

Hi Johnson,

Several ways to do that; for example, you can use WEKA's KnowledgeFlow as follows:

Training set will be passed into SimpleKMeans, after that prediction model gets built using J48 based on the generated clusters. All this process is evaluated using cross-validation.

NB: The attached image shows the flow process where FilteredClassifier is containing both cluster and classification algorithms.

HTH.

Samer

Silhouette analysis is based on the distance of the data point, it is very friendly to linear based clustering such as K-Mean. As a measure strategy, you can try to use it but its performance on density natural data might not be very ideal.

Meanwhile, if you want to know whether the number of components is a correct choice, may be you can try Variational Bayesian Gaussian Mixture. This is a deviation of traditional GMM which could automatically output the best component numbers.