Update README.md

doc/README.md

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 &#8226 File management: In the initial phase, the algorithm ingests the <code>best_nanoloops</code> directory along with the <code>properties</code> directory. <br>
-&#8226 Common (user) parameters: all parameters common to the entire (loop or curve) clustering analysis.
-&#8226 Loop parameters: all parameters common to the analysis of a clustering associated with a loop (best nanoloops generated after the second processing step).
-&#8226 Curve parameters: all parameters common to the analysis of a clustering associated with a curve (raw SSPFM measurement channel).
+&#8226 Common (user) parameters: all parameters common to the entire (loop or curve) clustering analysis. Two distinct clustering algorithms exist: one for loops and the other for curves, specified with the <code>object</code> parameter.<br>
+&#8226 Loop parameters: all parameters common to the analysis of a clustering associated with a loop (best nanoloops generated after the second processing step). <br>
+&#8226 Curve parameters: all parameters common to the analysis of a clustering associated with a curve (raw SSPFM measurement channel). <br>
 &#8226 Save and plot parameters: Pertaining to the management of display and the preservation of outcomes. <br>
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@@ -1725,7 +1725,7 @@ The script can be executed directly using the executable file: <code><a href="ht
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 &#8226 Loop: The entirety of data stemming from the best nanoloops, both in the on field and off field modes, is extracted from the files residing within the <code>best_nanoloops</code> directory (with the function <code>extract_loop_data</code> of <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/file_clustering.py">utils/file_clustering.py</a></code> script). <br>
 For piezoresponse loop analysis, vertical offset measurements in the off field mode and the dimensions of the mappings are drawn from the files within the <code>properties</code> directory (with <code>extract_properties</code> function of the script <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/nanoloop_to_hyst/file.py">utils/nanoloop_to_hyst/file.py</a></code>). <br>
-For piezoresponse loop analysis, the coupled measurements are subsequently generated through the process of differential analysis of on field and off field measurements, with the flexibility to incorporate the vertical offset in the off field mode, a component influenced by the sample's surface contact potential (section <a href="https://github.com/CEA-MetroCarac/PySSPFM/tree/main/doc#vi4d---differential-analysis-of-on-and-off-field-hysteresis">VI.4.d) - Differential analysis of on and off field hysteresis</a> in the documentation).<br>
+For piezoresponse loop analysis, the coupled measurements are subsequently generated through the process of differential analysis of on field and off field measurements, using the <code>gen_coupled_data</code> function from the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/file_clustering.py">utils/file_clustering.py</a></code> script, with the flexibility to incorporate the vertical offset in the off field mode, a component influenced by the sample's surface contact potential (section <a href="https://github.com/CEA-MetroCarac/PySSPFM/tree/main/doc#vi4d---differential-analysis-of-on-and-off-field-hysteresis">VI.4.d) - Differential analysis of on and off field hysteresis</a> in the documentation).<br>
 For a deeper understanding of the input file management, please refer to the relevant section in the documentation: <a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/doc/README.md#iii2b---second-step-of-data-analysis">III.2.b) - Second Step of Data Analysis</a>
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@@ -1736,7 +1736,9 @@ For a deeper understanding of the input file management, please refer to the rel
 #### VIII.3.c) Treatment
 
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-Initially, following data extraction, a loop is constructed. If multiple measures are specified in the <code>label_meas</code> parameter, they are normalized between 0 and 1 and concatenated together in the <code>gen_loop_data</code> function of the script. To analyze ferroelectric as well as electrostatic effects, the quality of clusterization can be enhanced by composing amplitude with phase rather than simply relying on piezoresponse <a href="#ref28">[28]</a>. To study nanomechanical properties under in situ material polarization, resonance frequency and quality factor loops can be selected (for elastic and dissipative properties, respectively). This can be particularly relevant, for example, in the study of relaxor ferroelectric materials <a href="#ref7">[7]</a>. For each of the modes (on field, off field, and eventually coupled), and for each of the nanoloops associated with each data point, a cluster is assigned using the machine learning K-Means or GMM methodology. To accomplish this, we import the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">KMeans</a> and <a href="https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html">GMM</a> functions from <a href="https://scikit-learn.org/stable/modules/clustering.html#clustering">sklearn.cluster</a>. A reference cluster is established, identified as the one encompassing the maximum number of data points. The index assigned to the other clusters is then computed as the distance between their centroid and that of the reference cluster, respectively. In other words, the clustering indexing provides the user with information about the separation (determined with quantitative data) of each cluster relative to the reference cluster. Subsequently, an average loop for each cluster is computed.
+&#8226 Initially, following data extraction, loops and curves are constructed using the <code>gen_loop_data</code> and <code>curve_extraction</code> functions from the <code><a href="https://github.com/CEA-MetroCarac/PySSPFM/blob/main/PySSPFM/utils/file_clustering.py">utils/file_clustering.py</a></code> script. The user can choose to normalize the vectors between 0 and 1 with the <code>relative</code> parameter. The clustering then depends more on the shape of the vectors than on their intensity. If multiple measures are specified in the <code>label_meas</code> parameter, they are normalized between 0 and 1 and concatenated together in the <code>gen_loop_data</code> function of the script. <br>
+&#8226 Specifically for the loop procedure, to analyze ferroelectric as well as electrostatic effects, the quality of clusterization can be enhanced by composing amplitude with phase rather than simply relying on piezoresponse <a href="#ref28">[28]</a>. To study nanomechanical properties under in situ material polarization, resonance frequency and quality factor loops can be selected (for elastic and dissipative properties, respectively). This can be particularly relevant, for example, in the study of relaxor ferroelectric materials <a href="#ref7">[7]</a>. For each of the modes (on field, off field, and eventually coupled), and for each of the nanoloops associated with each data point, the clustering procedure is performed. <br>
+&#8226 For both loop and curve procedures, if the <code>pca</code> parameter is selected by the user, an initial 2-dimensional Principal Component Analysis (PCA) can be performed on the set of vectors. This allows each vector to be represented as a point with coordinates in a plane, thereby facilitating the visualization of multiple data points and the subsequent clustering step. Next, a cluster is assigned using the machine learning K-Means or GMM methodology with the <code>method</code> parameter. To accomplish this, we import the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html">KMeans</a> and <a href="https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html">GMM</a> functions from <a href="https://scikit-learn.org/stable/modules/clustering.html#clustering">sklearn.cluster</a>. A reference cluster is established, identified as the one encompassing the maximum number of data points. The index assigned to the other clusters is then computed as the distance between their centroid and that of the reference cluster, respectively. In other words, the clustering indexing provides the user with information about the separation (determined with quantitative data) of each cluster relative to the reference cluster. Subsequently, an average loop for each cluster is computed.
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 #### VIII.3.d) Figures