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DETAILS ABOUT http://data.wordlift.io/wl0216/entity/cluster_analysis

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wordpress:content "<p><strong>Cluster analysis or clustering</strong> is the task of grouping a set of objects in such a way that objects in the same group (called cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis used in many fields, including <span id="urn:local-text-annotation-us4uvl0p9484ccjp8x7lmsua8e8mzvcs" class="textannotation disambiguated wl-thing" itemid="http://data.wordlift.io/wl0216/entity/machine_learning">machine learning</span>, pattern recognition, image analysis, information retrieval, and bioinformatics.</p> <p>We can work with <strong>cluster analysis using <span id="urn:enhancement-e504ad99-ef3e-4253-b02e-e21754500db3" class="textannotation disambiguated wl-thing" itemid="http://data.wordlift.io/wl0216/entity/machine_learning">machine learning</span></strong> to <strong>analyze queries</strong>. In this case, we can use different techniques to cluster queries.</p> <p>Let’s review them together:<b></b></p> <ul> <li aria-level="1"><b>Supervised text classification</b><span style="font-weight: 400;"> when we know the correct output class for each text in a sample dataset.</span></li> <li style="font-weight: 400;" aria-level="1"><b>Unsupervised text classification</b><span style="font-weight: 400;"> zero-shot is one of these techniques where the algorithm observes samples from classes that were not observed during training and needs to predict the class they belong to, we use it for instance for intent classification. You can try it out from </span><a href="https://api-docs.wordlift.io/swagger-ui/?urls.primaryName=classification#/Classifier/classifyUsingPost"><span style="font-weight: 400;">here</span></a><span style="font-weight: 400;"><span style="font-weight: 400;"> using a WordLift’s key. </span></span></li> <li style="font-weight: 400;" aria-level="1"><span style="font-weight: 400;"><span style="font-weight: 400;"><b></b></span></span><span style="font-weight: 400;"><b></b></span><b><span id="urn:enhancement-5613d093-f057-4038-b231-13879116c87a" class="textannotation disambiguated wl-thing" itemid="http://data.wordlift.io/wl0216/entity/named-entity_recognition">Named Entity Recognition</span> </b><span style="font-weight: 400;"><span style="font-weight: 400;">we can extract entities from queries as we do on <a href="http://longtail.wordlift.io">longtail.wordlift.io<b></b></a></span></span><span style="font-weight: 400;"><b></b></span><b></b></li> <li style="font-weight: 400;" aria-level="1"><b>K-Means clustering </b><span style="font-weight: 400;">one of the simplest and most popular unsupervised <span id="urn:enhancement-59807d4e-e29e-4710-9c03-d38f76c39913" class="textannotation disambiguated wl-thing" itemid="http://data.wordlift.io/wl0216/entity/machine_learning">machine learning</span> algorithms. K-Means averages the data by identifying a centroid for each group and by grouping all records in a limited number of clusters. A centroid is the imaginary center of each cluster. We used </span><a href="https://wordlift.io/blog/en/machine-learning-for-seo/"><span style="font-weight: 400;">K-Means here to analyze GSC data</span></a><span style="font-weight: 400;"><b></b></span></li> <li style="font-weight: 400;" aria-level="1"><b>Word <span id="urn:local-annotation-935787" class="disambiguated textannotation" itemid="http://data.wordlift.io/wl0216/entity/what_are_embeddings_">Embedding</span>s.</b><span style="font-weight: 400;"> Embedding encode the meaning of words using, real-valued vectors so that words that are closer in the vector space are expected to be similar in meaning, once meanings are turned into math we can use K-means to group them, we can use cosine similarity to evaluate how close they are or algorithms like PCA, UMAP and t-SNE to visualize them in a 2D or 3D space.</span></li> </ul> "^^xsd:string
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