Research Issues on Datamining

Data Mining refers to a set of methods applicable to large and complex databases to eliminate the randomness and discover the hidden pattern. Datamining (DM), also known as knowledge discovery from databases (KDD), is the extraction of new knowledge from huge databases. Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. Data mining tools can forecast the future trends and activities to support the decision of people. The scope of datamining is associated with Uncovering trends and patterns are a great power for the businesses of all sectors and industries.

Modern intrusion detection applications are confronted with a variety of issues. These applications must be reliable, extensible, manageable, and minimal in maintenance costs. Data mining-based intrusion detection systems (IDSs) have shown high accuracy, good generalisation to novel types of intrusion, and stable behaviour in a changing environment in recent years. The number of hidden layers in various neural network topologies is evaluated in order to discover the best neural network. The technique of attempting to discover instances of network attacks by comparing current behaviour to the expected actions of an intruder is known as misuse detection. Artificial neural networks have the ability to detect and classify network activity using data that is limited, incomplete, and nonlinear.

The main purpose of this work is to identify privacy and security concerns among cloud computing participants and consumers in a distributed environment. Techniques like Machine Learning, Natural Language Processing (NLP), and Data Mining are combined to automatically identify and uncover patterns from many sorts of materials. Predictive analytics is capable of dealing with both continuous and discontinuous changes. Classification, prediction, and to some extent, affinity analysis constitute the analytical methods employed in predictive analytics.

The contemporary study in text or document mining is focusing on syntactic components and the semantic environment. In order to accomplish this, and with the motivation gained from our previous research contributions, we investigated a mining model to classify documents based on the Order of Context, Concept, and Semantic Relations (OCCSR). The use of data mining techniques based on Cloud computing will enable users to retrieve meaningful information from virtually integrated data warehouses, lowering infrastructure and storage costs. Data mining can extract useful and potentially useful information from the cloud. Big Data is typically defined by three characteristics known as the 3Vs (Volume, Velocity and Variety). The surveys approaches, environments, and technologies in key areas for Big Data analytics capabilities and discusses how they aid in the development of analytics solutions for Clouds.

The clustering technique belongs to an unsupervised learning and it is used to discover a new set of categories. Grid-based clustering has the shortest processing time, which is typically determined by the size of the grid rather than the data. We compare the performance of three clustering algorithms: hierarchical clustering, density-based clustering, and K Means clustering.

The majority of current approaches to detecting misuse involve the use of rule-based expert systems to identify indicators of known attacks. We provide a brief overview of the use of various Artificial Intelligence techniques and their advancements in the design, development, and application of Intrusion Detection Systems (IDS) for protecting computer and communication networks from intruders. The goal of Knowledge Discovery in Data (KDD) is to extract information that is not obvious by using careful and detailed analysis and interpretation. To drive decisions and actions, analytics employs KDD, data mining, text mining, statistical and quantitative analysis, explanatory and predictive models, and advanced and interactive visualisation techniques.