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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 from experiments in physics, engineering, manufacturing, psychology, and medicine), and to information from economics and the social sciences.There are various methodologies of statistical data mining are as follows −Regression − In general, these techniques are used to forecast the value of a response (dependent) variable from new predictor (independent) variables, ... 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, given the recognition of mobile phones, GPS devices, Internet-based map services, weather services, and digital Earth, and satellite, RFID, sensor, wireless, and video technologies.There are several types of spatiotemporal data, moving-object data are important. For instance, animal scientists connect telemetry machinery on wildlife to explore ecological behavior, mobility managers embed ... 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 are as follows −Graph Pattern Mining − It is the mining of frequent subgraphs in one or a set of graphs. There are various approaches for mining graph patterns can be categorized into Apriori-based and pattern growth–based approaches.It can mine the set of closed graphs where a graph g is ... 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 at same time intervals (such as each minute, hour, or day).Time-series databases are famous in several applications including stock market analysis, economic and sales predicting, budgetary analysis, utility studies, inventory studies, revenue projections, workload projections, and process and quality service. They are beneficial for studying natural phenomena, mathematical and engineering ... 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 some interpretation as to why they are outliers is not very helpful.The interpretation of outliers can appear from definite subspaces that manifest the outliers or an assessment concerning the “outlierness” of the objects. Such interpretation can support users to learn the possible meaning and importance of the outliers.Data sparsity − ... 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 outliers.The sample can be used for training and testing. In various applications, the professionals can label only the normal objects, and several objects not connecting the model of normal objects are documented as outliers. There are different methods model the outliers and consider objects not connecting the model of outliers ... 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 are data components that cannot be combined in a given class or cluster. These are the data objects which have several behaviour from the general behaviour of different data objects. The analysis of this kind of data can be important to mine the knowledge.There are various challenges of outlier detection ... 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 of outliers. Most outlier detection methods are aimed at discovering global outliers.It can identify global outliers, an important issue is to discover an appropriate measurement of deviation concerning the application in question. There are several measurements are proposed, and, depends on these, outlier detection approaches are partitioned into multiple categories.Global ... 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 are data components that cannot be combined in a given class or cluster. These are the data objects which have several behaviour from the usual behaviour of different data objects. The analysis of this kind of data can be important to mine the knowledge.Outliers are different from noisy information. Noise ... 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 a group of constraints on examples (i.e., must-link or cannot-link constraints), how can we develop the k-means approach to satisfy such constraints? The COP-kmeans algorithm works as follows −Generate super instances for must-link constraints − It can calculate the transitive closure of the must-link constraints. Therefore, all must-link constraints are ... Read More