Dirichlet Process Mixture Models (DPMM)
Definition Dirichlet Process Mixture Models (DPMM) in AI marketing refers to a class of probabilistic models used to classify and predict consumer behavior based on observed data. The DPMM uses the Dirichlet Process, a statistical model that allows for a potentially infinite number of clusters, making it ideal for handling large or complex data sets. […]
Divisive Clustering
Definition Divisive Clustering in AI marketing refers to a top-down approach where all observations start in one cluster and splits are performed recursively as one moves down the hierarchy. It involves the use of algorithms that iteratively divide a dataset into separate and distinct groups based on certain criteria, such as customer behavior or preferences. […]
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Definition DBSCAN (Density-Based Spatial Clustering of Applications with Noise) in marketing refers to a data analysis AI algorithm used to identify and group similar data points in large datasets while excluding outliers. Essentially, it can segregate clusters of high density from sparser regions in the data. It’s a useful tool in market segmentation to detect […]
Differential Evolution
Definition Differential Evolution (DE) in AI is a method used in optimization problems, including marketing. It works by iteratively improving potential solutions and uses vectors to evaluate the performance of each solution. DE algorithms help in identifying the most effective marketing strategies by comparing and selecting the highest performing solutions. Key takeaway Differential Evolution (DE) […]
Density-Based Clustering
Definition Density-Based Clustering in AI marketing refers to a method of organizing data points into clusters based on their overall density across a specific space. In this approach, clusters are defined as dense regions of data points separated by areas of low data point density. It helps determine the intrinsic structures within data and manage […]
Data Augmentation
Definition Data Augmentation in AI marketing refers to the process of using artificial intelligence to increase the amount of data by adding variations to existing data. This could involve techniques such as rotation, flipping, and scaling to create a broader set for training machine learning models. The process improves the performance and ability of models […]
Denoising Autoencoders
Definition Denoising Autoencoders are a type of Artificial Intelligence (AI) model used in marketing for feature learning by reconstructing a clean input signal from a distorted version. Inherent to it is the introduction of noise to the input data during training, which forces the model to learn to retrieve original information. This application aids in […]
Deep Autoencoders
Definition Deep Autoencoders in marketing refers to a type of artificial intelligence algorithm that is used for feature extraction from large datasets. They work by compressing the input into a latent-space representation and then reconstructing the output from this representation. They are particularly useful for tasks like dimensionality reduction and anomaly detection in data. Key […]
Distributed Representation of Sentences (Doc2Vec)
Definition In AI marketing, the term ‘Distributed Representation of Sentences’ or Doc2Vec refers to an algorithm that generates a numeric representation of sentences, providing a fixed-size vector irrespective of the sentence length. It enhances the word2vec model to consider the broader context of a document. This allows for better understanding and assessment of the given […]
Document Embeddings
Definition Document Embeddings in AI marketing refer to the process of transforming entire text documents into numerical vectors. These embeddings are often created using machine learning algorithms, which analyze the semantics, context, and relationships in the text. The resulting vectors represent the overall content or meaning of the documents, making it easier to compare and […]
