What are the different aspects of mining methodology?

There are various aspects of mining methodology which are as follows −

Mining various and new kinds of knowledge − Data mining covers a broad spectrum of data analysis and knowledge discovery services, from data characterization and discrimination to relations and correlation analysis, classification, regression, clustering, outlier methods, sequence methods, and trend and computational analysis.

These services can use the same database in multiple ways and need the development of several data mining techniques. Because of the diversity of software, new mining services continue to emerge, developing data mining a powerful and fast-increasing field.

For instance, for effective knowledge discovery in data networks, integrated clustering and ranking can lead to the find of high-quality clusters and object ranks in high networks.

Mining knowledge in multidimensional space − When probing for knowledge in high data sets, it can analyse the information in multidimensional space. It can search for interesting patterns between sets of dimensions (attributes) at several levels of abstraction. Such mining is called a (exploratory) multidimensional data mining.

In several cases, data can be gathered or considered as a multidimensional data cube. Mining knowledge in cube area can increase the power and adaptability of data mining.

Data mining—an interdisciplinary effort − The power of data mining can be improved by integrating new techniques from several disciplines. For instance, it can mine records with natural language text, it creates sense to fuse data mining approaches with methods of data retrieval and natural language processing.

Boosting the power of discovery in a networked environment − Some data objects reside in a connected or interconnected environment, whether it be the Web, database association, files, or records. Semantic connection across several data objects can be used to benefit in data mining. Knowledge changed in one set of objects can be used to increase the discovery of knowledge in an “associated” or semantically connection group of objects.

Handling uncertainty, noise, or incompleteness of data − Data include noise, errors, exceptions, or ambiguity, or are incomplete. Errors and noise can confuse the data mining phase, leading to the derivation of erroneous designs. Data cleaning, data preprocessing, outlier detection and removal, and ambiguity reasoning are an instance of methods that required to be unified with the data mining process.

Pattern evaluation and pattern- or constraint-guided mining − It is not some patterns produced by data mining processes are interesting. It can creates a pattern interesting can vary from user to user. Hence, techniques are required to assess the interestingness of discovered patterns depends on subjective measures.

These calculate the value of patterns concerning a given user class, depends on user beliefs or expectations. Furthermore, by using interestingness measures or user-defined constraints to understand the discovery process, it can make more interesting patterns and decrease the search space.