Lecture 4
... Proteins are flexible. One would like to align proteins modulo the flexibility. Hinge and shear protein domain motions (Gerstein, Lesk , Chotia). Conformational flexibility in drugs. ...
... Proteins are flexible. One would like to align proteins modulo the flexibility. Hinge and shear protein domain motions (Gerstein, Lesk , Chotia). Conformational flexibility in drugs. ...
An Overview of Data Mining Techniques
... Mean - the average value for a given predictor. Median - the value for a given predictor that divides the database as nearly as possible into two databases of equal numbers of records. Mode - the most common value for the predictor. Variance - the measure of how spread out the values are from the av ...
... Mean - the average value for a given predictor. Median - the value for a given predictor that divides the database as nearly as possible into two databases of equal numbers of records. Mode - the most common value for the predictor. Variance - the measure of how spread out the values are from the av ...
A comparative study of some classification algorithms using WEKA
... limited number of combinatorial patterns generated in this way [6]. Many methods exist for classification in conventional data mining, mostly based on statistics: clustering, decision trees, association rules. LAD suggests a new way of analyzing data through combinatorial logic, Boolean functions, a ...
... limited number of combinatorial patterns generated in this way [6]. Many methods exist for classification in conventional data mining, mostly based on statistics: clustering, decision trees, association rules. LAD suggests a new way of analyzing data through combinatorial logic, Boolean functions, a ...
A MapReduce-Based k-Nearest Neighbor Approach for Big Data
... The k-NN algorithm is a non-parametric method that can be used for either classification and regression tasks. This section defines the k-NN problem, its current trends and the drawbacks to manage big data. A formal notation for the k-NN algorithm is the following: Let T R be a training dataset and T ...
... The k-NN algorithm is a non-parametric method that can be used for either classification and regression tasks. This section defines the k-NN problem, its current trends and the drawbacks to manage big data. A formal notation for the k-NN algorithm is the following: Let T R be a training dataset and T ...
Data Cleaning Missing Data
... • inconsistent with other recorded data and thus deleted • data not entered due to misunderstanding • certain data were not considered important at the time of collection • data format / contents of database changes in the course of the time changes with the corresponding enterprise organization SFU ...
... • inconsistent with other recorded data and thus deleted • data not entered due to misunderstanding • certain data were not considered important at the time of collection • data format / contents of database changes in the course of the time changes with the corresponding enterprise organization SFU ...
Evaluation on the meaning and value of
... Figure 3 : Information integration process of bank financial products marketing analysis system Data acquisition and integrated module design To meet the needs for customer information analysis, decision trees are widely applied. Now they can be used to determine the rules for the way a certain valu ...
... Figure 3 : Information integration process of bank financial products marketing analysis system Data acquisition and integrated module design To meet the needs for customer information analysis, decision trees are widely applied. Now they can be used to determine the rules for the way a certain valu ...
6: Review on data stream classification algorithm
... storage, computation and communication capabilities in computing systems. And for effective processing of stream data, new data structure, techniques, and algorithms are needed. Because we do not have finite amount of space to ...
... storage, computation and communication capabilities in computing systems. And for effective processing of stream data, new data structure, techniques, and algorithms are needed. Because we do not have finite amount of space to ...
Mining Frequent Spatio-Temporal Patterns from
... patterns inside a geographical area. These data are available from different sources, like GPS traces extracted from these devices or from internet sites where users voluntarily share their location among other information. Different knowledge can be extracted from these data depending on the analys ...
... patterns inside a geographical area. These data are available from different sources, like GPS traces extracted from these devices or from internet sites where users voluntarily share their location among other information. Different knowledge can be extracted from these data depending on the analys ...
Model-based cluster analysis for identifying - Acme
... the diverse and large number of users that they are required to interact with, make manual inspection infeasible. Many approaches have been proposed to find malicious users in review websites [16] and social media data [17, 30]. Most of these approaches attempt to identify suspicious users based on ...
... the diverse and large number of users that they are required to interact with, make manual inspection infeasible. Many approaches have been proposed to find malicious users in review websites [16] and social media data [17, 30]. Most of these approaches attempt to identify suspicious users based on ...
Spatial Clustering of Structured Objects
... (more specifically, machine learning), database technology, statistics and pattern recognition. A prominent example of DM task which has been investigated in several disciplines is clustering. It is a descriptive task which aims at identifying natural groups (or clusters) in data by relying on a give ...
... (more specifically, machine learning), database technology, statistics and pattern recognition. A prominent example of DM task which has been investigated in several disciplines is clustering. It is a descriptive task which aims at identifying natural groups (or clusters) in data by relying on a give ...
Communities and Hierarchical Structures in Dynamic Social Networks
... The second step clusters each static graph separately using an overlapping clustering algorithm, to produce Fuzzy Clusters. This step allows us to identify communities in the network but also its pivots (vertices shared by several clusters) while being insensitive to minor changes in the network as ...
... The second step clusters each static graph separately using an overlapping clustering algorithm, to produce Fuzzy Clusters. This step allows us to identify communities in the network but also its pivots (vertices shared by several clusters) while being insensitive to minor changes in the network as ...
Health Monitoring in an Agent-Based Smart Home
... As an example, consider our example string aaababbbbbaabccddcbaaa, ending in the phrase aaa. Within this phrase, the contexts that can be used for prediction are all suffixes within the phrase, except itself (i.e., aa, a, and the null context). From Figure 2 we see that an a occurs two out of the fi ...
... As an example, consider our example string aaababbbbbaabccddcbaaa, ending in the phrase aaa. Within this phrase, the contexts that can be used for prediction are all suffixes within the phrase, except itself (i.e., aa, a, and the null context). From Figure 2 we see that an a occurs two out of the fi ...
cse 6337 spring 1999 data mining
... • Steps Fig 1, p29 R[1] (Fig 1.3 in Fayyad) • Data Mining is one step in KDD process • KDD objective not usually clear or exact. May require time with customer understanding needs. • Data usually has problems - needs cleaning – Incorrect/missing data – Extract from multiple sources and compare – Del ...
... • Steps Fig 1, p29 R[1] (Fig 1.3 in Fayyad) • Data Mining is one step in KDD process • KDD objective not usually clear or exact. May require time with customer understanding needs. • Data usually has problems - needs cleaning – Incorrect/missing data – Extract from multiple sources and compare – Del ...
A Study on the accessible techniques to classify and predict
... [30]. The method is applied in the medical data set and minimal reduct set is found. The proposed method is compared with Quick Reduct, Entropy based Reduct and with Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization hybrided with Rough Set. The solutions provided by Quick Re ...
... [30]. The method is applied in the medical data set and minimal reduct set is found. The proposed method is compared with Quick Reduct, Entropy based Reduct and with Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization hybrided with Rough Set. The solutions provided by Quick Re ...
Lecture 3b
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
... Reason: data has not been collected for mining it Result: errors and omissions that don’t affect original purpose of data (e.g. age of customer) Typographical errors in nominal attributes values need to be checked for consistency Typographical and measurement errors in numeric attributes ...
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.