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Importance: Enables the extraction of useful information and insights from text data. Core questions: How can we effectively process and analyze large volumes of text? What techniques are there for understanding the meaning and sentiment of text? How is context maintained in text analysis?

Importance: Time series analysis is crucial for understanding trends and making forecasts in temporal data. Core questions: What are the best methods for analyzing time series data? How can we manage seasonality and trend components? What are the challenges in real-time analysis?

Importance: Data mining in bioinformatics helps in understanding biological processes and discovering new biomarkers. Core questions: How can data mining techniques be applied to genomic and proteomic data? What are the specific challenges associated with biological data? How is data mining contributing to personalized medicine?

Importance: Critical for identifying outliers, which can indicate fraud, network intrusions, or rare events. Core questions: How to distinguish noise from anomaly? What algorithms suit different anomaly types? How to balance false positives and false negatives?

Importance: Clustering algorithms are essential for identifying groups or clusters within large datasets. Core questions: What algorithm is most effective for which type of data? How can accuracy and efficiency be maximized? What are the scalability challenges?

Importance: With the explosion of data, big data analytics is needed to gain insights and make informed decisions. Core questions: How do we process and analyze petabytes of data effectively? What are the best practices for data storage and retrieval? How do the traditional data mining algorithms scale to big data?

Importance: Predictive modelling is crucial for forecasting future events based on historical data. Core questions: Which models give the best predictive accuracy? How can we ensure the model generalizes well to unseen data? What are the trade-offs between model complexity and interpretability?

Importance: Social network analysis is important for understanding the dynamics and structures of social relationships. Core questions: How can we identify influential nodes in a network? What are the metrics for measuring network properties? How can we detect communities within networks?

Importance: Association rule learning uncovers interesting relations between variables in large datasets. Core questions: How can we mine for strong rules in large databases? What measures are used to evaluate the strength of an association rule? How to deal with the challenges of multiple testing?