improving the efficiency of apriori algorithm in data mining
... emerging researches in today’s database technology. To extract the valuable data from large databases, it is necessary to explore the databases efficiently. It is the analysis step of the KDD (Knowledge Discovery and Data Mining) process. It is defined as the process of e ...
... emerging researches in today’s database technology. To extract the valuable data from large databases, it is necessary to explore the databases efficiently. It is the analysis step of the KDD (Knowledge Discovery and Data Mining) process. It is defined as the process of e ...
Impact of Evaluation Methods on Decision Tree Accuracy Batuhan
... Receiving large amount of data has given companies, governments and private people an opportunity to use these raw data and turn them into valuable information. For instance, companies have started improving their businesses by the help of data. Business intelligence (BI) and business analytics (BA) ...
... Receiving large amount of data has given companies, governments and private people an opportunity to use these raw data and turn them into valuable information. For instance, companies have started improving their businesses by the help of data. Business intelligence (BI) and business analytics (BA) ...
Everyday mining: Exploring sequences in event-based data
... This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal tren ...
... This thesis is concerned with the exploration of event-based data, and in particular the identification and analysis of sequences within them. Sequences are interesting in this context since they enable the understanding of the evolving character of event data records over time. They can reveal tren ...
Virtual models of indoor-air
... and shop facilities. The HVAC control system collected data for more than 60 parameters with a sampling interval of 1 min. Due to the type of data collection system available for this research, the values stored for all HVAC parameters are the (last-measured) point data rather than the 1-min average ...
... and shop facilities. The HVAC control system collected data for more than 60 parameters with a sampling interval of 1 min. Due to the type of data collection system available for this research, the values stored for all HVAC parameters are the (last-measured) point data rather than the 1-min average ...
Frameworks for entity matching: A comparison
... Methods for relational entity matching assume that each tuple represents an entity and all attribute values describe that entity. Sufficiently similar data values of two tuples imply that they are duplicates. Complex structured and XML data is semi-structured and is organized hierarchically. This com ...
... Methods for relational entity matching assume that each tuple represents an entity and all attribute values describe that entity. Sufficiently similar data values of two tuples imply that they are duplicates. Complex structured and XML data is semi-structured and is organized hierarchically. This com ...
Finding Highly Correlated Pairs Efficiently with Powerful Pruning
... In statistical linguistics, one may view words as items and sentences as relations (baskets) to establish the connections among words. In time-series analysis, one may view events as items and certain time windows as baskets to discover the dependencies among events. Correlation analysis in marketba ...
... In statistical linguistics, one may view words as items and sentences as relations (baskets) to establish the connections among words. In time-series analysis, one may view events as items and certain time windows as baskets to discover the dependencies among events. Correlation analysis in marketba ...
Data Mining: Concepts and Techniques Solution Manual
... • Pattern interestingness measure: This primitive allows users to specify functions that are used to separate uninteresting patterns from knowledge and may be used to guide the mining process, as well as to evaluate the discovered patterns. This allows the user to confine the number of uninteresting ...
... • Pattern interestingness measure: This primitive allows users to specify functions that are used to separate uninteresting patterns from knowledge and may be used to guide the mining process, as well as to evaluate the discovered patterns. This allows the user to confine the number of uninteresting ...
Chapter 22: Advanced Querying and Information Retrieval
... A cross-tab is a table where values for one of the dimension attributes form the row headers, values for another dimension attribute form the column headers Other dimension attributes are listed on top Values in individual cells are (aggregates of) the values of the dimension attributes that ...
... A cross-tab is a table where values for one of the dimension attributes form the row headers, values for another dimension attribute form the column headers Other dimension attributes are listed on top Values in individual cells are (aggregates of) the values of the dimension attributes that ...
data mining - a domain specific analytical tool for decision
... It consists of finding a model that describes significant dependencies between variables. Dependency models exist at two levels: (1) the structural level of the model specifies which variables are locally dependent on each other and (2) the quantitative level of the model specifies the strengths of ...
... It consists of finding a model that describes significant dependencies between variables. Dependency models exist at two levels: (1) the structural level of the model specifies which variables are locally dependent on each other and (2) the quantitative level of the model specifies the strengths of ...
Deep web - AllThesisOnline
... the words into the “topics” and the document is the collection of different topics. The task of parameter estimation in the model is what the topic and which document have topic in what proportion. The deep web sources are mostly sparse, therefore one motive to use the LDA due to sparseness of deep ...
... the words into the “topics” and the document is the collection of different topics. The task of parameter estimation in the model is what the topic and which document have topic in what proportion. The deep web sources are mostly sparse, therefore one motive to use the LDA due to sparseness of deep ...
Intelligent knowledge discovery on building energy and indoor
... indoor climate quality (ICQ). Indoor climate quality is determined by three aspects: indoor air quality (IAQ), thermal comfort conditions and hygrometric aspects (Corgnati et al. 2011). A high indoor climate quality can not only increase the health, ...
... indoor climate quality (ICQ). Indoor climate quality is determined by three aspects: indoor air quality (IAQ), thermal comfort conditions and hygrometric aspects (Corgnati et al. 2011). A high indoor climate quality can not only increase the health, ...
Entropy-Balanced Bitmap Tree for Shape-Based
... measuring similarity of shapes in the aforementioned feature spaces. Popular approaches depend, to a degree, on the feature space. In scale-space methods, the predominant methods involve finding inflection point correspondence between objects. Some approaches measure the similarity through deformati ...
... measuring similarity of shapes in the aforementioned feature spaces. Popular approaches depend, to a degree, on the feature space. In scale-space methods, the predominant methods involve finding inflection point correspondence between objects. Some approaches measure the similarity through deformati ...
Chapter 1 - users.cs.umn.edu
... One of the fundamental assumptions of statistical analysis is that the data samples are independently generated: like successive tosses of coin, or the rolling of a die. However, in the analysis of spatial data, the assumption about the independence of samples is generally false. In fact, spatial da ...
... One of the fundamental assumptions of statistical analysis is that the data samples are independently generated: like successive tosses of coin, or the rolling of a die. However, in the analysis of spatial data, the assumption about the independence of samples is generally false. In fact, spatial da ...
Fuzzy Decision Tree for Data Mining of Time Series Stock
... overload in human decision making process. The numerical salary, for example, may be perceived in linguistic terms such as high, average and low. Linguistic terms are simple forms of fuzzy values but generally their membership functions are unknown and need to be determined. One way of determining m ...
... overload in human decision making process. The numerical salary, for example, may be perceived in linguistic terms such as high, average and low. Linguistic terms are simple forms of fuzzy values but generally their membership functions are unknown and need to be determined. One way of determining m ...
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.