Found 422 Articles for Data Mining

Data Mining The process of extracting the data from a huge dataset that can be used for analysis and benefit of the organisation. This process helps in identifying patterns and managing relationship among the data to predict business problems. Data attributes An attribute can be defined as characteristics or property of an object. Object is described by attributes set and is referred to as a record of entity. Entity is described by a fraction of data i.e. attributes. For Example:In a Student database. (Name, id, Roll_no, Marks) are the attributes in provided database. Types of Attributes Nominal Attribute It only ... Read More

Association rule mining helps us to find relationships among large dataset. In Multidimensional association, Multidimensional association rule comprises of more than one aspect Numeric attributes should be discretized. Attributes can be unmitigated or quantitative. Quantitative characteristics are numeric and consolidate pecking order. Three approaches in mining multidimensional association rules are − Using static discretization of quantitative attributes Discretization happens earlier to mining and is static. Discretized attributes are treated as absolute and use an algorithm called apriori algorithm to search for all k-frequent predicate sets(k or k+1 table scans are required). Each subset of a frequent predicate set ... Read More

In this article, we will discuss concepts of Multilevel Association Rule mining and its algorithms, applications, and challenges. Data mining is the process of extracting hidden patterns from large data sets. One of the fundamental techniques in data mining is association rule mining. To identify relationships between items in a dataset, Association rule mining is used. These relationships can then be used to make predictions about future occurrences of those items. Multilevel Association Rule mining is an extension of Association Rule mining. Multilevel Association Rule mining is a powerful tool that can be used to discover patterns and trends. Association ... Read More

Data transformation is an essential phase in the data mining process. It entails transforming unprocessed data into an analytically useful format. Data transformation seeks to enhance the consistency and relevance of the data for the desired analysis while reducing redundancy and improving data quality. The significance of data transformation in data mining as well as some typical data transformation techniques will be covered in this article. Importance of Data Transformation in Data Mining Data transformation is an essential element of data mining for several reasons. Firstly, analyzing unstructured, erroneous or incomplete raw data can be challenging and time-consuming. Therefore, the ... Read More

In machine learning and data mining, clustering is a frequently used approach that seeks to divide a dataset into subsets or clusters based on their similarities or differences. Applications like consumer segmentation, fraud detection, and anomaly detection frequently employ clustering models. Nevertheless, there is no one method that works for all datasets and clustering algorithms, therefore assessing the effectiveness of clustering models is not always simple. In this blog article, we'll go through the important elements of assessing the effectiveness of clustering models, including several evaluation metrics and methods. Understanding the Basics of Clustering Let's quickly go over the fundamentals ... Read More

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