Data mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns. Theoreticians and practitioners are continually seeking improved techniques to make the process more efficient, cost-effective, and accurate. Many other terms carry a similar or slightly different meaning to data mining such as knowledge mining from data, knowledge extraction, data/pattern analysis data dredging....

The Complex data types require advanced data mining techniques. Some of the  Complex data types are sequence Data which includes the Time-Series, Symbolic Sequences, and Biological Sequences. The additional preprocessing steps are needed for data mining of these complex data types....

The discrete wavelet transform (DWT) is a signal processing technique that transforms linear signals. The data vector X  is transformed into a numerically different vector, Xo, of wavelet coefficients when the DWT is applied. The two vectors X and Xo must be of the same length. When applying this technique to data reduction, we consider n-dimensional data tuple, that is, X = (x1,x2,…,xn), where n is the number of attributes present in the relation of the data set....

Data mining is the process of discovering patterns and relationships in large datasets using techniques such as machine learning and statistical analysis. The goal of data mining is to extract useful information from large datasets and use it to make predictions or inform decision-making. Data mining is important because it allows organizations to uncover insights and trends in their data that would be difficult or impossible to discover manually....

PCY was developed by Park, Chen, and Yu. It is used for frequent itemset mining when the dataset is very large....

A cluster is the collection of data objects which are similar to each other within the same group. The data objects of a  cluster are dissimilar to data objects of other groups or clusters....

Artificial intelligence uses two different kinds of machine learning algorithms: classic and adaptive. These algorithms have changed throughout time and are now often utilized in many different applications....

The histogram is old method used to plot the attributes in a graph. Histo means to plot and gram means chart. So basically histogram is a graph of the poles. It is one of the effective methods to summarize the distribution of a given attribute....

Dimension reduction is a necessary step in the effective analysis of massive high-dimensional datasets. It may be the main objective in Data Mining for the analysis and visualization of the high-dimensional data or it may be an intermediate step that enables some other analysis such as clustering....

The security of our computer systems and data is at continual risk. The extensive growth of the Internet and the increasing availability of tools and tricks for intruding and attacking networks have prompted intrusion detection and prevention to become a critical component of networked systems....

Data mining: The process of extracting useful information from a huge amount of data is called Data mining. Data mining is a tool that is used by humans to discover new, accurate, and useful patterns in data or meaningful relevant information for the ones who need it....

Data Mining plays a major role in segregating useful data from a heap of big data. By analyzing the patterns and peculiarities, it enables us to find the relationship between data sets. When the unprocessed raw data is processed into useful information, it can be applied to enhance the growth of many fields we depend on in our day-to-day life....