What is Data Mining ?
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier: Data object that does not comply with the general behavior of the data Noise or exception? ...
... Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier: Data object that does not comply with the general behavior of the data Noise or exception? ...
methodologies of knowledge discovery from data and data mining
... Using modern technologies is related to gathering large amounts of data in all areas of company operation. Proper use of the data, often stored in large databases, to build process knowledge and realize constant improvement, as well as determination of the development strategy, is one of the challen ...
... Using modern technologies is related to gathering large amounts of data in all areas of company operation. Proper use of the data, often stored in large databases, to build process knowledge and realize constant improvement, as well as determination of the development strategy, is one of the challen ...
Shiniphy - Visual Data Mining of movie recommendations
... out of them. Other sites like Jinni and Pandora have more interactive and visually appealing results but these use datasets specifying the genome of the song/film which are painstakingly made by hand. Many a times these results are good, but they do not give any feedback as to why a movie is being r ...
... out of them. Other sites like Jinni and Pandora have more interactive and visually appealing results but these use datasets specifying the genome of the song/film which are painstakingly made by hand. Many a times these results are good, but they do not give any feedback as to why a movie is being r ...
UNIT-1 DATA WAREHOUSING 1. What are the uses of multifeature
... 2. Compare OLTP and OLAP Systems. (Apr/May 2008), (May/June 2010) 3. What is data warehouse metadata? (Apr/May 2008) 4. Explain the differences between star and snowflake schema. (Nov/Dec 2008) 5. In the context of data warehousing what is data transformation? (May/June 2009) 6. Define Slice and Dic ...
... 2. Compare OLTP and OLAP Systems. (Apr/May 2008), (May/June 2010) 3. What is data warehouse metadata? (Apr/May 2008) 4. Explain the differences between star and snowflake schema. (Nov/Dec 2008) 5. In the context of data warehousing what is data transformation? (May/June 2009) 6. Define Slice and Dic ...
Designing KDD-Workflows via HTN
... have optional “Nothing to Do” methods (as shown for CleanMissingValues in Fig. 3c). Most of the preprocessing methods are recursive, handling one attribute at a time until nothing is left to be done. CleanMissingValues has two different recursive methods, the choice is made by the planner depending ...
... have optional “Nothing to Do” methods (as shown for CleanMissingValues in Fig. 3c). Most of the preprocessing methods are recursive, handling one attribute at a time until nothing is left to be done. CleanMissingValues has two different recursive methods, the choice is made by the planner depending ...
New Ensemble Methods For Evolving Data Streams
... credit card transactional flows, etc. An important fact is that data may be evolving over time, so we need methods that adapt automatically. Under the constraints of the Data Stream model, the main properties of an ideal classification method are the following: high accuracy and fast adaption to cha ...
... credit card transactional flows, etc. An important fact is that data may be evolving over time, so we need methods that adapt automatically. Under the constraints of the Data Stream model, the main properties of an ideal classification method are the following: high accuracy and fast adaption to cha ...
Subjective interestingness in exploratory data mining
... IMs). However, under different names and guises (e.g. ‘objective function’, ‘quality function’, ‘score function’, or ‘utility function’), the concept of interestingness remains central to all EDM prototypes, such as clustering, dimensionality reduction, community detection in networks, subgroup disc ...
... IMs). However, under different names and guises (e.g. ‘objective function’, ‘quality function’, ‘score function’, or ‘utility function’), the concept of interestingness remains central to all EDM prototypes, such as clustering, dimensionality reduction, community detection in networks, subgroup disc ...
Combined Data Mining and Decision Support
... Academic achievement depends on the consistency between individual’s features and demands of school. Therefore, the problem of high school failure has its roots mostly in an inappropriate choice of school. The choice of school or profession is a multiattribute decision-making process in which the ch ...
... Academic achievement depends on the consistency between individual’s features and demands of school. Therefore, the problem of high school failure has its roots mostly in an inappropriate choice of school. The choice of school or profession is a multiattribute decision-making process in which the ch ...
23-datamining - Computer Science Department
... Clustering points: Stock-{UP/DOWN} Similarity Measure: Two points are more similar if the events described by them frequently happen together on the same day. We used association rules to quantify a similarity measure. ...
... Clustering points: Stock-{UP/DOWN} Similarity Measure: Two points are more similar if the events described by them frequently happen together on the same day. We used association rules to quantify a similarity measure. ...
the method of time granularity determination on time series
... With the constant progress of science and technology, data size is increasing in the areas of social life and industrial production. And people are gradually aware of the potential value of data, causing a flood of big data and data mining. Data in real life is mostly related to time, called time se ...
... With the constant progress of science and technology, data size is increasing in the areas of social life and industrial production. And people are gradually aware of the potential value of data, causing a flood of big data and data mining. Data in real life is mostly related to time, called time se ...
Audience Segment Expansion Using Distributed In
... Figure 1: Modeling the segment “sports enthusiast” using rule-based approaches (a), regression approaches (b) and clustering approaches (c). a warehouse. Most data warehouses do not natively support mining and analytics, so the data is usually exported and then processed by external applications (su ...
... Figure 1: Modeling the segment “sports enthusiast” using rule-based approaches (a), regression approaches (b) and clustering approaches (c). a warehouse. Most data warehouses do not natively support mining and analytics, so the data is usually exported and then processed by external applications (su ...
Data_Types - University of California, Riverside
... – As a practical matter, real values can only be measured and represented using a finite number of digits. – Continuous attributes are typically represented as floatingpoint variables. ...
... – As a practical matter, real values can only be measured and represented using a finite number of digits. – Continuous attributes are typically represented as floatingpoint variables. ...
Student Performance Prediction by Using Data Mining Classification
... and selected. Popular WEKA classifiers (with their default settings unless specified otherwise) are used in the experimental study, including a rule learner (OneR), a common decision tree algorithm C4.5 (J48), a neural network (MultiLayer Perceptron), and a Nearest Neighbour algorithm (IBk). These c ...
... and selected. Popular WEKA classifiers (with their default settings unless specified otherwise) are used in the experimental study, including a rule learner (OneR), a common decision tree algorithm C4.5 (J48), a neural network (MultiLayer Perceptron), and a Nearest Neighbour algorithm (IBk). These c ...
Towards Effective Data Preprocessing for Classification Using WEKA
... for feature selection and 20 for clustering and association rule mining. In this paper, the Iris data set from UCI data sets will be used to demonstrate different activities on WEKA tool in the KDD process as show below. ...
... for feature selection and 20 for clustering and association rule mining. In this paper, the Iris data set from UCI data sets will be used to demonstrate different activities on WEKA tool in the KDD process as show below. ...
Data Mining in Social Networks - Purdue University :: Computer
... splits on those aggregated values. For example, for a numeric attribute such as birth year, it searches over splits such as MEAN(birthyr) > x, PROPORTION(birthyr > x) > y, MAXIMUM(birthyr) > y, MINIMUM(birthyr) > x, and COUNT(birthyr > x) > y. Our current approach continues partitioning the trainin ...
... splits on those aggregated values. For example, for a numeric attribute such as birth year, it searches over splits such as MEAN(birthyr) > x, PROPORTION(birthyr > x) > y, MAXIMUM(birthyr) > y, MINIMUM(birthyr) > x, and COUNT(birthyr > x) > y. Our current approach continues partitioning the trainin ...
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.