Density-based Cluster Analysis for Identification of Fire Hot Spots in
... performing a density-based cluster analysis on the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD14ML active fire data set for a 12 year period between 2003 and 2014. Feature subset selection was done using an AWK script written to extract the latitude and longitude fields from the data s ...
... performing a density-based cluster analysis on the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD14ML active fire data set for a 12 year period between 2003 and 2014. Feature subset selection was done using an AWK script written to extract the latitude and longitude fields from the data s ...
On Demand Classification of Data Streams
... Such processes lead to data which often grow without limit and are referred to as data streams [3]. One important data mining problem which has been studied in the context of data streams is that of classification [5]. The main thrust on data stream mining in the context of classification has been t ...
... Such processes lead to data which often grow without limit and are referred to as data streams [3]. One important data mining problem which has been studied in the context of data streams is that of classification [5]. The main thrust on data stream mining in the context of classification has been t ...
Change-Point Detection in Time-Series Data by Direct Density
... A common limitation of the above-mentioned approaches is that they rely on pre-specified parametric models such as probability density models, autoregressive models, and state-space models. Thus, these methods tend to be less flexible in real-world change-point detection scenarios. The primal purpos ...
... A common limitation of the above-mentioned approaches is that they rely on pre-specified parametric models such as probability density models, autoregressive models, and state-space models. Thus, these methods tend to be less flexible in real-world change-point detection scenarios. The primal purpos ...
An Interactive Approach to Mining Gene Expression Data
... Hierarchical approaches organize objects into a hierarchy of nested clusters called a dendrogram. Depending on how the dendrogram is formed, hierarchical approaches can be further divided into agglomerative methods [2, 6, 24] and divisive methods [1, 12, 16]. Hierarchical approaches typically have t ...
... Hierarchical approaches organize objects into a hierarchy of nested clusters called a dendrogram. Depending on how the dendrogram is formed, hierarchical approaches can be further divided into agglomerative methods [2, 6, 24] and divisive methods [1, 12, 16]. Hierarchical approaches typically have t ...
Recent Progress on Selected Topics in Database Research
... We argue that one of the keys to mining data streams is online mining of changes. For example, consider a stream of regular updates of various aircrafts’ positions. An air traffic controller may be interested in the clusters of the aircrafts at each moment. However, instead of checking details for “ ...
... We argue that one of the keys to mining data streams is online mining of changes. For example, consider a stream of regular updates of various aircrafts’ positions. An air traffic controller may be interested in the clusters of the aircrafts at each moment. However, instead of checking details for “ ...
Affordance mining: Forming perception through action Linköping University Post Print
... spatial relationships but also in terms of object possibilities for action [4]. The work presented here demonstrates an embodied approach to constructing an affordance based representation of the world. Data mining algorithms are useful for efficiently identifying correlations in large symbolic data ...
... spatial relationships but also in terms of object possibilities for action [4]. The work presented here demonstrates an embodied approach to constructing an affordance based representation of the world. Data mining algorithms are useful for efficiently identifying correlations in large symbolic data ...
Constraint-Based Mining of Formal Concepts in - LIRIS
... Function cutting cuts out a couple (X, Y ) with the first cutter H[i] that satisfies the following constraints. First, (X, Y ) must have a non empty intersection with H[i]. If it is not the case, cutting is called with the next cutter. Before cutting (X, Y ) in (X\a, Y ), we have to check the monotoni ...
... Function cutting cuts out a couple (X, Y ) with the first cutter H[i] that satisfies the following constraints. First, (X, Y ) must have a non empty intersection with H[i]. If it is not the case, cutting is called with the next cutter. Before cutting (X, Y ) in (X\a, Y ), we have to check the monotoni ...
Cluster Analysis for Large, High
... Cluster analysis represents one of the most versatile methods in statistical science. It is employed in empirical sciences for the summarization of datasets into groups of similar objects, with the purpose of facilitating the interpretation and further analysis of the data. Cluster analysis is of pa ...
... Cluster analysis represents one of the most versatile methods in statistical science. It is employed in empirical sciences for the summarization of datasets into groups of similar objects, with the purpose of facilitating the interpretation and further analysis of the data. Cluster analysis is of pa ...
Here - Wirtschaftsinformatik und Maschinelles Lernen, Universität
... Next to the plenary and semi-plenary talks, our scientific program accommodates 130 contributions, 16 of them in the LIS workshop. As expected, the lion’s share among the contributions comes from Germany, followed by Poland, but we have contributions from all over the world, stretching from Portugal ...
... Next to the plenary and semi-plenary talks, our scientific program accommodates 130 contributions, 16 of them in the LIS workshop. As expected, the lion’s share among the contributions comes from Germany, followed by Poland, but we have contributions from all over the world, stretching from Portugal ...
application of c4.5 algorithm for detection of cooperatives failure in
... Abstract – Cooperative is one of the actors in Indonesia's economy is expected to become pillar of the economy in Indonesia. Based on the stastistical data on site Ministry of Cooperatives and Small and Menengah Enterprises are many cooperatives at the provincial level have failed. The purpose of th ...
... Abstract – Cooperative is one of the actors in Indonesia's economy is expected to become pillar of the economy in Indonesia. Based on the stastistical data on site Ministry of Cooperatives and Small and Menengah Enterprises are many cooperatives at the provincial level have failed. The purpose of th ...
Symbolic Data Analysis - Institute of Statistical Science, Academia
... • Four theorems of convergence needed to be proved on any extended method to Symbolic Data • Models of models • Law of parameters of laws and Laws of vectors of laws. • Copulas needing. • Optimisation in non supervised learning (hierarchical and pyramidal clustering). ...
... • Four theorems of convergence needed to be proved on any extended method to Symbolic Data • Models of models • Law of parameters of laws and Laws of vectors of laws. • Copulas needing. • Optimisation in non supervised learning (hierarchical and pyramidal clustering). ...
Highly Robust Methods in Data Mining
... without using a prior knowledge about the group membership of each observation. It is often used as an exploratory technique and can be also interpreted as a technique for a dimension reduction of complex multivariate data. Cluster analysis assumes the data to be fixed (non-random) without the ambit ...
... without using a prior knowledge about the group membership of each observation. It is often used as an exploratory technique and can be also interpreted as a technique for a dimension reduction of complex multivariate data. Cluster analysis assumes the data to be fixed (non-random) without the ambit ...
Multivariate Discretization by Recursive Supervised Bipartition of
... methods with others, in a single framework. The main advantage of partitioning methods lies in their intrinsic capacity for providing the user with an underlying structure of the analysed data. However, this structural gain may be balanced by an information loss. The first experiment aims at evaluati ...
... methods with others, in a single framework. The main advantage of partitioning methods lies in their intrinsic capacity for providing the user with an underlying structure of the analysed data. However, this structural gain may be balanced by an information loss. The first experiment aims at evaluati ...
A Methodology for Sensitive Attribute Discrimination Prevention in
... or category. It involves denying to members of one group opportunities that are available to other groups. There is a list of antidiscrimination acts, which are laws designed to prevent discrimination on the basis of a number of attributes (e.g., race, religion, gender, nationality, disability, mari ...
... or category. It involves denying to members of one group opportunities that are available to other groups. There is a list of antidiscrimination acts, which are laws designed to prevent discrimination on the basis of a number of attributes (e.g., race, religion, gender, nationality, disability, mari ...
Multivariate discretization by recursive supervised
... methods with others, in a single framework. The main advantage of partitioning methods lies in their intrinsic capacity for providing the user with an underlying structure of the analysed data. However, this structural gain may be balanced by an information loss. The first experiment aims at evaluati ...
... methods with others, in a single framework. The main advantage of partitioning methods lies in their intrinsic capacity for providing the user with an underlying structure of the analysed data. However, this structural gain may be balanced by an information loss. The first experiment aims at evaluati ...
Tinnitus Retraining Therapy
... THE KNOWLEDGE GAINED will result in the design foundations of a decision support system to aid in tinnitus treatment effectiveness for TRT. ...
... THE KNOWLEDGE GAINED will result in the design foundations of a decision support system to aid in tinnitus treatment effectiveness for TRT. ...
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.