In statistical data mining techniques, it is created for the effective handling of large amounts of data that are generally multidimensional and possibly of several complex types.There are several well-established statistical methods for data analysis, especially for numeric data. These methods have been used extensively to scientific records (e.g., records ... Read More

Spatiotemporal data mining define the process of finding patterns and knowledge from spatiotemporal data. An instances of spatiotemporal data mining contains finding the developmental history of cities and lands, uncovering weather designs, forecasting earthquakes and hurricanes, and deciding global warming trends.Spatiotemporal data mining has become important and has far-extending implications, ... Read More

Graphs defines a more general class of mechanism than sets, sequences, lattices, and trees. There is a wide range of graph applications on the internet and in social networks, data networks, biological web, bioinformatics, chemical informatics, computer vision, and multimedia and content retrieval. The applications of mining graphs and networks ... Read More

A sequence is an ordered list of events. Sequences can be divided into three groups, based on the features of the events they define as follows −Similarity Search in Time-Series DataA time-series data set includes sequences of integer values acquired over repeated computation of time. The values are generally measured ... Read More

There are various challenges of outlier detection in high-dimensional data are as follows −Interpretation of outliers − They must be able to not only identify outliers, but also support an interpretation of the outliers. Because several features (or dimensions) are contained in a high-dimensional data set, identifying outliers without supporting ... Read More

There are various methods of outlier detection is as follows −Supervised Methods − Supervised methods model data normality and abnormality. Domain professionals tests and label a sample of the basic data. Outlier detection can be modeled as a classification issue. The service is to understand a classifier that can identify ... Read More

An outlier is a data object that deviates essentially from the rest of the objects, as if it were produced by a different structure. For ease of presentation, it can define data objects that are not outliers as “normal” or expected information. Similarly, it can define outliers as “abnormal” data.Outliers ... Read More

There are various types of outliers in data mining are as follows −Global Outliers − In a given data set, a data object is a global outlier if it deviates essentially from the rest of the information set. Global outliers are known as point anomalies, and are the easiest type ... Read More

An outlier is a data object that diverge essentially from the rest of the objects, as if it were produced by a several mechanism. For ease of presentation, it can define data objects that are not outliers as “normal” or expected information. Usually, it can define outliers as “abnormal” data.Outliers ... Read More

There are various techniques are required to handle specific constraints. The general principles of handling hard and soft constraints which are as follows −Handling Hard Constraints − A general methods for handling difficult constraints is to strictly regard the constraints in the cluster assignment procedure. Given a data set and ... Read More