Aviation Data Mining - University of Minnesota Morris Digital Well
... data from these reports, we first have to identify the overall picture of the data. This process is called text classification. Text classification is a general term and there are several different methods of text classification. The research outlined in this paper classifies text by using some prel ...
... data from these reports, we first have to identify the overall picture of the data. This process is called text classification. Text classification is a general term and there are several different methods of text classification. The research outlined in this paper classifies text by using some prel ...
Neural Networks in Data Mining
... Figure 3. Image of data-mining process. Data mining is the business of answering questions that you’ve not asked yet. Data mining reaches deep into databases. Data mining tasks can be classified into two categories: Descriptive and predictive data mining. Descriptive data mining provides information ...
... Figure 3. Image of data-mining process. Data mining is the business of answering questions that you’ve not asked yet. Data mining reaches deep into databases. Data mining tasks can be classified into two categories: Descriptive and predictive data mining. Descriptive data mining provides information ...
Exploring Geospatial Music Listening Patterns in Microblog Data
... with a meaningful color-mapping. The first approach presented in this paper organizes tweets in a number of clusters, where a cluster may represent, e.g., genre, mood, country, or language and each cluster is assigned a specific color. As genre classification is the most traditional way of organizin ...
... with a meaningful color-mapping. The first approach presented in this paper organizes tweets in a number of clusters, where a cluster may represent, e.g., genre, mood, country, or language and each cluster is assigned a specific color. As genre classification is the most traditional way of organizin ...
Enhancing evolutionary instance selection algorithms by means of
... The K-Nearest Neighbors classifier (K-NN) [13,48,58] can be greatly enhanced when using these data reduction techniques. It is a nonparametric classifier which simply uses the entire input data set to establish the classification rule. Thus, the effectiveness of the classification process performed by t ...
... The K-Nearest Neighbors classifier (K-NN) [13,48,58] can be greatly enhanced when using these data reduction techniques. It is a nonparametric classifier which simply uses the entire input data set to establish the classification rule. Thus, the effectiveness of the classification process performed by t ...
Data Mining In Education
... between variables, in a data set with large number of variables. This may take the form of attempting to find out which variables are most strongly related/ associated with a single variable of particular interest. Broadly relationship mining is classified into four types: association rule mining, c ...
... between variables, in a data set with large number of variables. This may take the form of attempting to find out which variables are most strongly related/ associated with a single variable of particular interest. Broadly relationship mining is classified into four types: association rule mining, c ...
Generating a Diverse Set of High-Quality Clusterings
... Thus, the goal is this paper is to generate a set of k partitions that best represent all high-quality partitions as accurately as possible. Related Work. There are two main approaches in the literature for computing many high-quality, diverse partitions. However, both approaches focus only on a sp ...
... Thus, the goal is this paper is to generate a set of k partitions that best represent all high-quality partitions as accurately as possible. Related Work. There are two main approaches in the literature for computing many high-quality, diverse partitions. However, both approaches focus only on a sp ...
Mining Frequent Patterns Without Candidate Generation
... Mining can be performed in a variety of information repositories Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. Classification of data mining systems ...
... Mining can be performed in a variety of information repositories Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. Classification of data mining systems ...
An Efficient Multi-set HPID3 Algorithm based on RFM Model
... Data mining is generally thought of as the process of extracting hidden, previously unknown and potentially useful information from databases. Exploiting large volumes of data for superior decision making by looking for interesting patterns in the data has become a main task in today’s business envi ...
... Data mining is generally thought of as the process of extracting hidden, previously unknown and potentially useful information from databases. Exploiting large volumes of data for superior decision making by looking for interesting patterns in the data has become a main task in today’s business envi ...
Comparison of Feature Selection Techniques in
... validation classification error rate is used as a performance indicator for a data mining task for a selected feature subset [17]. In this research we use three filter techniques: ReliefF, Information Gain and Gain Ratio. The original ReliefF algorithm belongs to the family of algorithms Relief. A k ...
... validation classification error rate is used as a performance indicator for a data mining task for a selected feature subset [17]. In this research we use three filter techniques: ReliefF, Information Gain and Gain Ratio. The original ReliefF algorithm belongs to the family of algorithms Relief. A k ...
Energy saving in smart homes based on
... consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opp ...
... consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opp ...
Effectiveness of Data Preprocessing for Data Mining
... label is missing. This method is not very effective, unless the tuple contains several attributes with missing values. 2. Fill in the missing value manually: In general, this approach is time-consuming and may not be feasible given a large data set with many missing values. 3. Use a global constant ...
... label is missing. This method is not very effective, unless the tuple contains several attributes with missing values. 2. Fill in the missing value manually: In general, this approach is time-consuming and may not be feasible given a large data set with many missing values. 3. Use a global constant ...
A Predictive Model to Evaluate Student Performance - J
... attitudes towards learning, and investigated how the attitudes affect final student evaluation; they pursued a case study of lecture data analysis in which the correlations exist between student attitudes to learning such as attendance and homework, as effort, and the student examination scores, as a ...
... attitudes towards learning, and investigated how the attitudes affect final student evaluation; they pursued a case study of lecture data analysis in which the correlations exist between student attitudes to learning such as attendance and homework, as effort, and the student examination scores, as a ...
ROAM: Rule-and Motif-Based Anomaly Detection in Massive Moving
... There have been some prior work in the area of trajec1.1 Problem Definition The problem of anomaly tory prediction [16, 15]. Markov models or other sequendetection in moving object data is defined as follows. tial models can model a single trajectory and predict its The input data is a set of labele ...
... There have been some prior work in the area of trajec1.1 Problem Definition The problem of anomaly tory prediction [16, 15]. Markov models or other sequendetection in moving object data is defined as follows. tial models can model a single trajectory and predict its The input data is a set of labele ...
Game of Thrones : Text Analysis of the George R.R Martin`s book
... Terms are given a weight based on the inverse of the their frequency used . Concept linking is a way to find and display the terms that are highly associated with the selected term in the Terms table. The selected term is surrounded by the terms that correlate the strongest with it. ...
... Terms are given a weight based on the inverse of the their frequency used . Concept linking is a way to find and display the terms that are highly associated with the selected term in the Terms table. The selected term is surrounded by the terms that correlate the strongest with it. ...
Experiment No. 1
... Decision Tree learning is one of the most widely used and practical methods for inductive inference over supervised data. A decision tree represents a procedure for classifying categorical data based on their attributes. It is also efficient for processing large amount of data, so is often use in da ...
... Decision Tree learning is one of the most widely used and practical methods for inductive inference over supervised data. A decision tree represents a procedure for classifying categorical data based on their attributes. It is also efficient for processing large amount of data, so is often use in da ...
Data - UIC Computer Science
... Partition data set into clusters, and one can store cluster representation only ...
... Partition data set into clusters, and one can store cluster representation only ...
Data Mining – Past, Present and Future – A Typical Survey on Data
... data warehouse the data may or may not be present in the structured format. The structure of the data may be defined to make it compatible for processing. Hence in data mining; we also need to primarily concentrate on cleansing the data so as to make it feasible for further processing. The process o ...
... data warehouse the data may or may not be present in the structured format. The structure of the data may be defined to make it compatible for processing. Hence in data mining; we also need to primarily concentrate on cleansing the data so as to make it feasible for further processing. The process o ...
Integration of Data Mining and Relational Databases
... browsed but queries will always return empty data. In order to be able to execute prediction using a mining model, it must be trained with known cases by using the INSERT statement that will point to the source of the training data (like source of input rows in SQL). The behavior of this INSERT sta ...
... browsed but queries will always return empty data. In order to be able to execute prediction using a mining model, it must be trained with known cases by using the INSERT statement that will point to the source of the training data (like source of input rows in SQL). The behavior of this INSERT sta ...
Mining High Quality Association Rules Using - CEUR
... is a mechanism through which evolutionary algorithms form and maintain subpopulations or niches. Niching fosters the evolution of several different rules each covering a different part of the data being mined. This assists avoid the convergence of the population to a single rule resulting in the dis ...
... is a mechanism through which evolutionary algorithms form and maintain subpopulations or niches. Niching fosters the evolution of several different rules each covering a different part of the data being mined. This assists avoid the convergence of the population to a single rule resulting in the dis ...
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.