Data Mining
... 1. CEC07. Ability to learn and develop techniques of computing learning and design and implement applications and systems which use them, including those dedicated to automatic information and knowledge extraction from large data volums. 2. CEIS4. Ability to identify and analyze problems and design, ...
... 1. CEC07. Ability to learn and develop techniques of computing learning and design and implement applications and systems which use them, including those dedicated to automatic information and knowledge extraction from large data volums. 2. CEIS4. Ability to identify and analyze problems and design, ...
Marko Grobelnik, Dunja Mladenic, Mitja Jermol - ailab
... documents. Each of them is described in the following subsections. Since the used modeling techniques are well established in the research community, the evaluation part was mainly concentrated on correctness of the results and was performed in the loop with the end-user in the last phase of finaliz ...
... documents. Each of them is described in the following subsections. Since the used modeling techniques are well established in the research community, the evaluation part was mainly concentrated on correctness of the results and was performed in the loop with the end-user in the last phase of finaliz ...
Course Syllabus Data Warehousing
... Submit your home work covered with a sheet containing your name, number, course title and number, and type and number of the home work (e.g. tutorial, assignment, and project). Any completed homework must be handed in to my office (room IT ---) by 15:00 on the due date. After the deadline “zero” wil ...
... Submit your home work covered with a sheet containing your name, number, course title and number, and type and number of the home work (e.g. tutorial, assignment, and project). Any completed homework must be handed in to my office (room IT ---) by 15:00 on the due date. After the deadline “zero” wil ...
FCART: A New FCA-based System for Data Analysis and
... Another interesting option of our system is that of multicontext [16] artifact. In the most general case a multicontext can be considered as a network of contexts. In the particular case each context is assigned a specific time point, – then one can use this artifact for describing statements of a d ...
... Another interesting option of our system is that of multicontext [16] artifact. In the most general case a multicontext can be considered as a network of contexts. In the particular case each context is assigned a specific time point, – then one can use this artifact for describing statements of a d ...
Knowledge Discovery about Quality of Life Changes of Spinal Cord
... 4. Discussion, Conclusions and Future Work ClBRxE detects different groups of patients using their QoL perception over time. QoL in SCI is a very complex phenomenon, involving multidimensional factors as well as the patient’s own perception, and successful analysis requires not only data, but also a ...
... 4. Discussion, Conclusions and Future Work ClBRxE detects different groups of patients using their QoL perception over time. QoL in SCI is a very complex phenomenon, involving multidimensional factors as well as the patient’s own perception, and successful analysis requires not only data, but also a ...
Lecture Notes - Computer Science Department
... will result on a set of attributes that describe what interest us from the domain and what we want to use for the discovery process. According to this representation we can categorize the different kinds of datasets in two groups. The first kind are those that can be represented by a flat structure, ...
... will result on a set of attributes that describe what interest us from the domain and what we want to use for the discovery process. According to this representation we can categorize the different kinds of datasets in two groups. The first kind are those that can be represented by a flat structure, ...
Market Basket Analysis using Association Rule Learning
... basis of predictions made on the status of heart using heart disease data. The results of experiment showed that Apriori was best suited for this type of data. In 2010, a new algorithm SC-BF Multilevel was introduced as a better version of Apriori algorithm which was faster and efficient since it re ...
... basis of predictions made on the status of heart using heart disease data. The results of experiment showed that Apriori was best suited for this type of data. In 2010, a new algorithm SC-BF Multilevel was introduced as a better version of Apriori algorithm which was faster and efficient since it re ...
Survey on Using Data Mining Algorithms with on KDD CUP 99 Data
... million connection records, each with about 100 bytes. The two weeks of test data have around 2 million connection records. KDD training dataset consists of approximately 4,900,000 single connection vectors each of which contains 41 features and is labeled as either normal or an attack, with exactly ...
... million connection records, each with about 100 bytes. The two weeks of test data have around 2 million connection records. KDD training dataset consists of approximately 4,900,000 single connection vectors each of which contains 41 features and is labeled as either normal or an attack, with exactly ...
document
... have ask. So Data Mining another word for statistic? • We hope that the response will be resounding NO • The major difference is that statistical methods work with random data samples, whereas the data in databases is not necessarily random • The second difference is the size of the data set • The t ...
... have ask. So Data Mining another word for statistic? • We hope that the response will be resounding NO • The major difference is that statistical methods work with random data samples, whereas the data in databases is not necessarily random • The second difference is the size of the data set • The t ...
Slides
... something (as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically : the mathematical procedures (as finding the maximum of a function) involved in this.” ...
... something (as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically : the mathematical procedures (as finding the maximum of a function) involved in this.” ...
A Study of the Scaling up Capabilities of Stratified Prototype
... Nowadays, researchers in many fields such as marketing, medicine or biology have improved the way in which they acquire the data, allowing them to deal with greater and more difficult problems than before. It implies that data mining tools [2] must manage a higher number of data. Therefore, many class ...
... Nowadays, researchers in many fields such as marketing, medicine or biology have improved the way in which they acquire the data, allowing them to deal with greater and more difficult problems than before. It implies that data mining tools [2] must manage a higher number of data. Therefore, many class ...
Models and Algorithms for High-Performance Distributed Data Mining
... of interesting contexts that may stimulate further investigation in this field. In Distributed Data Mining models and algorithms, high-performance is not only an architecture-and-resource--oriented matter, but also it involves in designing innovative models, algorithms and techniques capable of deal ...
... of interesting contexts that may stimulate further investigation in this field. In Distributed Data Mining models and algorithms, high-performance is not only an architecture-and-resource--oriented matter, but also it involves in designing innovative models, algorithms and techniques capable of deal ...
Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics.Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Clustering can therefore be formulated as a multi-objective optimization problem. The appropriate clustering algorithm and parameter settings (including values such as the distance function to use, a density threshold or the number of expected clusters) depend on the individual data set and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of knowledge discovery or interactive multi-objective optimization that involves trial and failure. It will often be necessary to modify data preprocessing and model parameters until the result achieves the desired properties.Besides the term clustering, there are a number of terms with similar meanings, including automatic classification, numerical taxonomy, botryology (from Greek βότρυς ""grape"") and typological analysis. The subtle differences are often in the usage of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest. This often leads to misunderstandings between researchers coming from the fields of data mining and machine learning, since they use the same terms and often the same algorithms, but have different goals.Cluster analysis was originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by Zubin in 1938 and Robert Tryon in 1939 and famously used by Cattell beginning in 1943 for trait theory classification in personality psychology.