Recent advances in computational technology have made it possible to store and mine huge data sets, much larger than ever before. Data-mining techniques, which extract useful information from these massive data sets, have evolved into a mature technique which can be employed as an efficient predictive tool across broad scientific fields, including but not limited to molecular biology, astronomy, bioinformatics, physics and medicine. In this chapter, first we discuss an original method (fragment data mining, FDM), which mines the structural segments from the Protein Data Bank (PDB) and utilizes structural information by matching the sequence of these structural fragments with the aim of improving the prediction of secondary structure. We also discuss further improvements by combining FDM with the classical GOR V secondary structure prediction method, which is based on information theory and Bayesian statistics, coupled with evolutionary information from multiple sequence alignments. We also discuss a second approach that is newer, and more accurate for secondary structure predictions, the SPINE-X method based on a machine-learning methodology to predict secondary structures by mining protein sequences and structures. Our results strongly suggest that data mining can be an efficient and accurate approach for secondary structure prediction in proteins. The last part of the chapter discusses applications of data mining to the problem of optimizing protein crystallization conditions. Data mining can be used to improve the yield and quality of protein crystals, and thus aid in solving protein structures by X-ray crystallography. There is a vast amount of data for protein structures, sequences and crystallization conditions that can be mined to aid in structure prediction and structure determination.