How can you explain clustering in data mining interviews?

La clusterización de datos es una de las técnicas más fascinantes dentro del campo del Machine Learning, ya que proporciona insights e hipótesis sobre el comportamiento de los datos. Consiste en identificar grupos de observaciones de manera que cada grupo tenga características distintivas que lo diferencien de los demás. A estos grupos de observaciones se les asigna una etiqueta representativa en un proceso conocido como perfilamiento. Para determinar estas agrupaciones, se utiliza una medida de similitud que se adapta al tipo de datos con el que se esté trabajando; por ejemplo, para observaciones numéricas se puede utilizar la distancia euclidiana y para observaciones categóricas la distancia de Jaccard o Hamming.

Clustering is simply detecting hidden similar set of data namely groups using data features. Based on your perspective wholen data may be splitted into different combinations of clusters so that there is no ground truth. Due to this nature this approach is an unsupervised method.

Clustering is a technique that groups similar data together based on their characteristics without predefined labels. For example, in customer purchase data, clustering can identify segments of customers with similar buying patterns. This helps businesses understand customer behavior and tailor marketing strategies. Using algorithms like k-means, data points are assigned to clusters based on their distance to cluster centers. Clustering enables data exploration and pattern discovery in an unsupervised manner, aiding decision-making in personalized marketing and customer analysis.

Clustering is a way of grouping similar items or data points together based on certain characteristics. In easy terms, think of clustering as sorting things into piles where items within the same pile are more similar to each other than to those in other piles. Real-life example: Grocery Shopping Imagine you’re at a grocery store, and you want to organize your shopping list efficiently. You might group items based on categories like fruits, vegetables, dairy, and so on. Within each category, you further organize items based on similarities. For instance, in the fruit category, you might create subgroups for citrus fruits and berries. In this scenario: • Clustering: Grouping similar items (fruits, vegetables, etc.) together.

Clustering is a way to infer groupings based on some definition of "likeness" among features in a dataset, with "likeness" being some measure of distance among observations. Clustering is a technique for which multiple distance-calculation algorithms exist.

Clustering mostly useful for uncovering non-obvious insight, especially when you haven’t quite understood your customer. In marketing or sales domain, clustering is highly useful for: - discovering the natural customer groups based on underlying behavior - research about current market exploration and expansion - pinpoint troubled group of users for improving customer service

La agrupación en clusters es útil cuando no tienes una variable target definida. Cada registro se puede asociar a un cluster y de ahí que se pueda trabajar con éstos como si fueran la variable dependiente. Además de que generar clusters da cierta comprensión de cómo se comportan los datos.

Clustering is useful because it helps uncover hidden patterns and structures within data without prior knowledge or labeled examples. Here are a few reasons why clustering is valuable: 1) Customer segmentation 2) Outlier Detection (any customer with unique needs) 3) Decision support: Making data-driven decisions.

Clustering is useful for several reasons in various fields, including data analysis, machine learning, and real-world applications. Here are some key reasons why clustering is valuable: 1. Pattern Recognition: • Clustering helps identify inherent patterns and structures in data by grouping similar observations together. This can reveal hidden relationships or insights that might not be apparent initially. 2. Data Compression and Summarization: • Clustering can simplify large and complex dataset 3. Data Preprocessing for Machine Learning: • Before applying machine learning algorithms, clustering can be used for data preprocessing. It helps handle missing values, identify relevant features, and create meaningful input for models.

There are many cases where you dont have prior data. For example if you are launching a new product, there is no old data. So you have to do some sample test and see which customers are interested in the product. Then do clustering to see which cluster do these customers fall into and then market to those in that segment.

1. K-Means 2. Hierarchical Clustering 3. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) 4. Gaussian Mixture Model (GMM) 5. Agglomerative Clustering 6. Mean Shift 7. OPTICS (Ordering Points To Identify the Clustering Structure) 8. Affinity Propagation 9. Spectral Clustering 10. Fuzzy C-Means

Clustering algorithms are used to group similar data points together, uncovering patterns and structures in datasets. Popular algorithms include k-means, which partitions data into k clusters using centroid-based assignment. Hierarchical clustering builds a cluster hierarchy based on merging or splitting criteria. Density-based algorithms, like DBSCAN, group data points based on density. Gaussian Mixture Models capture data distribution using probability models. Spectral clustering leverages graph theory to identify clusters. Affinity Propagation uses message passing to find exemplars representing clusters. Each algorithm has strengths and weaknesses, such as scalability, shape flexibility, or handling noise.

K-means is the most used. However, newer clustering techniques like auto-encoders are more powerful for many use cases like anti-money laundering.

Metrics should match with the data on hand. Test the cluster results and then constantly review the outcomes. If the results are not good, redo the clusters

En las técnicas de clustering, y en general en las de aprendizaje no supervisado, resulta complicado determinar si la agrupación realizada es acertada o no, ya que a diferencia del aprendizaje supervisado no tenemos una variable de respuesta con la cual comparar. No obstante se tienen algunas métricas como: el análisis de silueta, el índice de Calinski y el PSI (Population Stability Index). Las primeras dos ayudan a conocer la calidad de los clústers generados, es decir qué tan similares son entre sí y que tan disimilares son enyte ellos así como a tener una noción de la cantidad óptima de clusters que se pueden considerar. La última métrica ayuda a evaluar la evolución del cluster en el tiempo y conocer en qué momento se debe recalibrar.

The "distance" function used in clusters are more than 100 in R and Python. So knowing what is used and when is important. Try implementing them and learn by programming

Clustering is a technique used to group similar data points together based on their characteristics. It's a form of unsupervised learning and is used in various applications, e.g. grouping together similar cases of fraud attacks as preparation towards building a training set. A deep process of gap analysis would often be needed, to verify that a true connection emerges within the items clustered and that the model chosen makes sense. Sometimes the line between cluster and clutter is very fine...

Las agrupaciones pueden responder preguntas mediante un perfilamiento. Es decir, analizar las variables y encontrar aquellas que para cada grupo son diferentes. De esta manera los grupos generados tendrán características únicas que los diferenciarán entre ellos y así generar estadística descriptiva que vaya enfocada en la pregunta de negocio.

Para toda entrevista de trabajo, ya sea que esté enfocada en la parte de data o no, se puede emplear la metodología STAR. Con esta metodología se puede responder de manera estructurada y por pasos a una situación o tarea en específico, en este caso algoritmos de clustering. Las siglas aluden a la Situación, Tarea, Acción y Resultado. Cada uno de éstos pasos pueden ser respondidos con: qué se quiere resolver mediante el algoritmo de clusterización, para qué se quiere resolver mediante clusterización, cómo se resolverá o elegirá el algoritmo de clusterización y finalmente el impacto de la solución.