Data Preprocessing
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is ―smeared‖ Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is ―smeared‖ Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
Data - Electrical Engineering and Computer Science
... Normalize input data: Each attribute falls within the same range ...
... Normalize input data: Each attribute falls within the same range ...
Fast Monte-Carlo Algorithms for Matrix Multiplication
... Ak and Kleinberg’s HITS algorithm Hypertext Induced Topic Selection (HITS) A link analysis algorithm that rates Web pages for their authority and hub scores. Authority score: an estimate of the value of the content of the page. Hub score: an estimate of the value of the links from this page to othe ...
... Ak and Kleinberg’s HITS algorithm Hypertext Induced Topic Selection (HITS) A link analysis algorithm that rates Web pages for their authority and hub scores. Authority score: an estimate of the value of the content of the page. Hub score: an estimate of the value of the links from this page to othe ...
Associative Classification Based on Incremental Mining (ACIM)
... In association rule discovery several incremental algorithms have been developed such as Fast Update (FUP) [2], FUP2 [3], Insertion, Deletion and Updating [4], Galois Lattice theory [5], and New Fast Update (NFUP) [6]. However, in classification data mining especially associative classification [7] ...
... In association rule discovery several incremental algorithms have been developed such as Fast Update (FUP) [2], FUP2 [3], Insertion, Deletion and Updating [4], Galois Lattice theory [5], and New Fast Update (NFUP) [6]. However, in classification data mining especially associative classification [7] ...
LOF: Identifying Density-based Local Outlier
... sampling for approximate clustering and outlier detection in large datasets. IEEE Transactions on Knowledge and Data Engineering, 2003. A. Lazarevic, L. Ert ¨oz, V. Kumar, A. Ozgur, and J. Srivastava. A comparative study of anomaly detection schemes in network intrusion detection. In SDM, 2003. S. R ...
... sampling for approximate clustering and outlier detection in large datasets. IEEE Transactions on Knowledge and Data Engineering, 2003. A. Lazarevic, L. Ert ¨oz, V. Kumar, A. Ozgur, and J. Srivastava. A comparative study of anomaly detection schemes in network intrusion detection. In SDM, 2003. S. R ...
081 - CE Sharif
... Stream data mining tasks Multi-dimensional on-line analysis of streams Mining outliers and unusual patterns in stream data Clustering data streams Classification of stream data ...
... Stream data mining tasks Multi-dimensional on-line analysis of streams Mining outliers and unusual patterns in stream data Clustering data streams Classification of stream data ...
Slide 1 - Department of Computer Science
... Definition of Apriori Algorithm Steps to perform Apriori Algorithm Apriori Algorithm Examples Pseudo Code for Apriori Algorithm Apriori Advantages/Disadvantages ...
... Definition of Apriori Algorithm Steps to perform Apriori Algorithm Apriori Algorithm Examples Pseudo Code for Apriori Algorithm Apriori Advantages/Disadvantages ...
Data Mining, Data Warehousing and Knowledge Discovery
... Quinlan’s depth-first strategy builds the decision tree in a depth-first fashion, by considering all possible tests that give a decision and selecting the test that gives the best information gain. It hence eliminates tests that are inconclusive. SLIQ (Supervised Learning in Quest) developed in the ...
... Quinlan’s depth-first strategy builds the decision tree in a depth-first fashion, by considering all possible tests that give a decision and selecting the test that gives the best information gain. It hence eliminates tests that are inconclusive. SLIQ (Supervised Learning in Quest) developed in the ...
Commercially Available Data Mining Tools used in the Economic
... directly (as shown in Fig. 4). To integrate a data mining system in the computer system of a company it must be able to retrieve data from the computer system’s database using their analysis techniques and performing tasks such as clustering, association, prediction, regression, etc. [3]. Following ...
... directly (as shown in Fig. 4). To integrate a data mining system in the computer system of a company it must be able to retrieve data from the computer system’s database using their analysis techniques and performing tasks such as clustering, association, prediction, regression, etc. [3]. Following ...
ppt
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
... store cluster representation (e.g., centroid and diameter) only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multidimensional index tree structures There are many choices of clustering definitions and clustering algorithms ...
What is Data Mining
... decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then g ...
... decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then g ...
Algorithm for Tracing Visitors` On-Line Behaviors for Effective Web
... features in identifying the user level of interest. The second feature used is based on site topology and cookies. Frequency value, session identification, path completion are also identified using this UILP algorithm [11]. In UILP (i) During data cleaning process, explicit image and multimedia requ ...
... features in identifying the user level of interest. The second feature used is based on site topology and cookies. Frequency value, session identification, path completion are also identified using this UILP algorithm [11]. In UILP (i) During data cleaning process, explicit image and multimedia requ ...
The effect of data pre-processing on the performance of Artificial
... The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active are ...
... The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active are ...
Feature Selection: A Practitioner View
... Selecting few features from the original set of features based on measures like correlation, entropy and mutual information etc. Among the features and the target variable (if one exists) is called feature selection. Feature selection is also known as variable selection, attribute selection or varia ...
... Selecting few features from the original set of features based on measures like correlation, entropy and mutual information etc. Among the features and the target variable (if one exists) is called feature selection. Feature selection is also known as variable selection, attribute selection or varia ...
Mining Spatio-Temporal Datasets: Relevance, Challenges
... The development of efficient techniques for combined spatial and temporal data mining is an open and challenging issue within the research community. In this chapter we investigate this topic and discuss current trends in the area. In particular, given the visual aspect of spatial data, we discuss h ...
... The development of efficient techniques for combined spatial and temporal data mining is an open and challenging issue within the research community. In this chapter we investigate this topic and discuss current trends in the area. In particular, given the visual aspect of spatial data, we discuss h ...
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.