Privacy-preserving data mining is an application of data mining research in response to privacy security in data mining. It is called a privacy-enhanced or privacy-sensitive data mining. It deals with obtaining true data mining results without disclosing the basic sensitive data values.Most privacy-preserving data mining approaches use various form of transformation on the data to implement privacy preservation. Generally, such methods decrease the granularity of description to keep privacy.For instance, they can generalize the data from single users to users groups. This reduction in granularity causes loss of data and probably of the utility of the data mining results. This ... Read More

Recommender systems can use a content-based approach, a collaborative approach, or a hybrid approach that combines both content-based and collaborative methods.Content-based − In the content-based approach recommends items that are same to items the customer preferred or queried in the previous. It depends on product features and textual item definition.In content-based methods, it is calculated based on the utilities assigned by the similar user to different items that are same. Many systems target on recommending items including textual data, including websites, articles, and news messages. They view for commonalities between items. For movies, they can view for same genres, directors, ... Read More

An intrusion can be represented as any set of services that threaten the integrity, confidentiality, or accessibility of a network resource (e.g., user accounts, file systems, system kernels, etc).Intrusion detection systems and intrusion prevention systems both monitor network traffic and system performance for malicious activities. The former produces documents whereas the latter is located in-line and is able to actively avoid/block intrusions that are identified.The advantage of an intrusion prevention system are to recognize malicious activity, log data about said activity, tries to block/stop activity, and document activity. Data mining methods can support an intrusion detection and prevention system to ... Read More

Data mining is the process of finding useful new correlations, patterns, and trends by transferring through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.The security of our computer systems and information is at continual risk. The huge growth of the web and the increasing accessibility of tools and tricks for intruding and attacking webs have prompted intrusion detection and avoidance to become ... Read More

There are various roles of data mining in science and engineering are as follows −Data warehouses and data preprocessing − Data preprocessing and data warehouses are important for data exchange and data mining. It is making a warehouse requires discovering means for resolving inconsistent or incompatible information collected in several environments and at multiple time periods.This needed reconciling semantics, referencing systems, mathematics, measurements, efficiency, and precision. Methods are needed for integrating data from heterogeneous sources and for identifying events.Mining complex data types − Numerical data sets are heterogeneous in nature. They generally contains semi-structured and unstructured data, including multimedia data ... Read More

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