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In multimedia datasets, time-series analysis is used to identify patterns over time in time-dependent data such as video or audio. Techniques include Fourier Transforms for frequency analysis or Hidden Markov Models for sequence prediction.

In multimedia data mining, feature extraction involves processing multimedia content to extract meaningful descriptors that can be used for classification, indexing, or similarity searches, such as color histograms in images or MFCC in audio files.

Association rule mining in multimedia datasets involves discovering interesting relations between variables in large databases. It's applicable in video surveillance for detecting frequent patterns of activity or in e-commerce for recommending related products based on user history.

In multimedia data mining, text mining extracts valuable information from text embedded within multimedia files, such as captions, subtitles, or metadata. Techniques like Natural Language Processing (NLP) and sentiment analysis are applied to understand and interpret the textual content.

Dimensionality reduction techniques, like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE), are used in multimedia datasets to reduce the number of random variables under consideration, and can be divided into feature selection and feature extraction.

Content-based retrieval in multimedia data mining refers to retrieving relevant multimedia files from a large database based on the content - images by shape or color features, audio by spectral features. It contrasts with metadata-based search and involves complex feature matching techniques.

Multimedia data indexing is the process of creating indexes that allow rapid searching and retrieval of multimedia content based on extracted features. Techniques such as inverted indexing or spatial indexing (e.g., R-trees for images) are often used.