EDM Program Educational Data Mining is a leading international forum for high-quality research that mines data sets to answer educational research questions that shed light on the learning process. These data sets may originate from a variety of learning contexts, including learning management systems, interactive learning environments, intelligent tutoring systems, educational games, and data-rich learning activities.
See Article History Alternative Title: The field combines tools from statistics and artificial intelligence such as neural networks and machine learning with database management to analyze large digital collections, known as data sets.
Data mining is widely used in business insurance, banking, retailscience research astronomy, medicineand government security detection of criminals and terrorists. The proliferation of numerous large, and sometimes connected, government and private databases has led to regulations to ensure that individual records are accurate and secure from unauthorized viewing or tampering.
Most types of data mining are targeted toward ascertaining general knowledge about a group rather than knowledge about specific individuals—a supermarket is less concerned about selling one more item to one person than about selling many items to many people—though pattern analysis also may be used to discern anomalous individual behaviour such as fraud or other criminal activity.
Origins and early applications As computer storage capacities increased during the s, many companies began to store more transactional data. The resulting record collections, often called data warehouses, were too large to be analyzed with traditional statistical approaches.
Several computer science conferences and workshops were held to consider how recent advances in the field of artificial intelligence AI —such as discoveries from expert systemsgenetic algorithmsmachine learningand neural networks—could be adapted for knowledge discovery the preferred term in the computer science community.
This was also the period when many early data-mining companies were formed and products were introduced. One of the earliest successful applications of data mining, perhaps second only to marketing research, was credit-card - fraud detection.
However, the wide variety of normal behaviours makes this challenging; no single distinction between normal and fraudulent behaviour works for everyone or all the time. Every individual is likely to make some purchases that differ from the types he has made before, so relying on what is normal for a single individual is likely to give too many false alarms.
One approach to improving reliability is first to group individuals that have similar purchasing patterns, since group models are less sensitive to minor anomalies.
Modeling and data-mining approaches Model creation The complete data-mining process involves multiple steps, from understanding the goals of a project and what data are available to implementing process changes based on the final analysis. The three key computational steps are the model-learning process, model evaluation, and use of the model.
This division is clearest with classification of data. Model learning occurs when one algorithm is applied to data about which the group or class attribute is known in order to produce a classifier, or an algorithm learned from the data.
The classifier is then tested with an independent evaluation set that contains data with known attributes. If the model is sufficiently accurate, it can be used to classify data for which the target attribute is unknown. Data-mining techniques There are many types of data mining, typically divided by the kind of information attributes known and the type of knowledge sought from the data-mining model.
Predictive modeling Predictive modeling is used when the goal is to estimate the value of a particular target attribute and there exist sample training data for which values of that attribute are known.
An example is classification, which takes a set of data already divided into predefined groups and searches for patterns in the data that differentiate those groups. These discovered patterns then can be used to classify other data where the right group designation for the target attribute is unknown though other attributes may be known.
For instance, a manufacturer could develop a predictive model that distinguishes parts that fail under extreme heat, extreme cold, or other conditions based on their manufacturing environmentand this model may then be used to determine appropriate applications for each part.
Another technique employed in predictive modeling is regression analysis, which can be used when the target attribute is a numeric value and the goal is to predict that value for new data.
Descriptive modeling Descriptive modeling, or clustering, also divides data into groups. With clustering, however, the proper groups are not known in advance; the patterns discovered by analyzing the data are used to determine the groups.
For example, an advertiser could analyze a general population in order to classify potential customers into different clusters and then develop separate advertising campaigns targeted to each group.
Fraud detection also makes use of clustering to identify groups of individuals with similar purchasing patterns. Pattern mining Pattern mining concentrates on identifying rules that describe specific patterns within the data.
Market-basket analysis, which identifies items that typically occur together in purchase transactions, was one of the first applications of data mining. For example, supermarkets used market-basket analysis to identify items that were often purchased together—for instance, a store featuring a fish sale would also stock up on tartar sauce.Explore research at Microsoft, a site featuring the impact of research along with publications, products, downloads, and research careers.
Google is deeply engaged in Data Management research across a variety of topics with deep connections to Google products. We are building intelligent systems to discover, annotate, and explore structured data from the Web, and to surface them creatively through Google products, such as Search (e.g., structured snippets, Docs, and many others).The overarching goal is to create a plethora of.
Resource Library. Access the latest white papers, research webcasts, case studies and more covering a wide range of topics like Mobile, Cloud and Data Analyitcs. IEEE CASE will be under the motto Knowledge-based Automation.
It will gather experts from academia and industry to report on recent developments, trends and research pfmlures.com invites submissions of high-quality research and industry papers describing original and unpublished work.
Now a days we have very interesting research topics in data mining called "Social Network Analysis". It is the very interesting research based on the social networking sites. ICDM Call for Paper.
The Aim of the Conference Topics of the conference Program Committee Deadlines. The Aim of the Conference. This conference is the thirteen conference in a series of industrial conferences on Data Mining that will be held on yearly basis.