本文探讨了数据挖掘领域的五大挑战：可扩展性、高维数据处理、异构复杂数据、数据所有权与分布以及非传统分析任务。面对海量数据集，算法必须具备可扩展性；随着维度增加，计算复杂度急剧上升；同时，处理不同类型的数据、地理分布的数据所有权及自动化生成与评估大量假设成为当前数据分析师的重要课题。
1. Scalability
If data mining algorithms are to handle these massive data sets, then they must be scalable.
2. High Dimensionality
For some data analysis algorithms, the computational complexity increases rapidly as the dimensionality increases.
3. Heterogeneous and Complex Data
Dealing with data with not the same type.
4. Data Ownership and Distribution
Data is geographically distributed among resources belonging to multiple entities.
5. Non-traditional Analysis
The traditional statistical approach is based on a hypothesize-and-test paradigm.
Current data analysis tasks often require the generation and evaluation of thousands of hypotheses, and consequently, the development of some data mining techniques has been motivated by the desire to automate the process of hypothesis generation and evaluation.
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