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Data mining is a process of inferring knowledge from such huge data. Data Mining has three major components Clustering or Classification, Association Rules and Sequence Analysis. By simple definition, in classification/clustering analyze a set of data and generate a set of grouping rules which can be used to classify future data. Data mining is the process is to extract information from a data set and transform it into an understandable structure. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns. Data mining involves six common classes of tasks. Anomaly detection, Association rule learning, Clustering, Classification, Regression, Summarization. Classification is a major technique in data mining and widely used in various fields. Classification is a data mining (machine learning) technique used to predict group membership for data instances. In this paper, we present the basic classification techniques. Several major kinds of classification method including decision tree induction, Bayesian networks, k-nearest neighbor classifier, the goal of this study is to provide a comprehensive review of different classification techniques in data mining.

Deep learning seems to be getting the attention right now by hitting all areas of machine learning and data science.

deep learining with big data become hot topic in data mining.

Sentiment Analysis, Deep Learning, Text Analytics...

Sentiment analysis ,deep learning & cognitive science,Image analytics with deep learning

Like it was suggested to use Machine Learning(ML) in Finance by Leo and the ML genres that was described by Maysam, you can look at your background and see where ML can be applicable. For me, i've used ML in electricity prediction for effective power planning and currently using it now for Health care and Information Security. In summary, this will lead you to interdisciplinary research and i so much believe you will enjoy it.

Cognitive Computing - data mining is a hot topic lately even it is not a newest topic.

Interpretability is a topic with very fast growing interest. Many data mining/machine learning techniques are described as "black box" because explanations of their inner workings are very difficult use in a meaningful way. More and more work is happening on extracting meaning from the internals, and not only the prediction/decision of classification.

You can check the following link:

http://nevonprojects.com/latest-data-mining-projects-topics-ideas/

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