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A Data Association Algorithm for Multiple Object Tracking in Video Sequences

A Data Association Algorithm for Multiple Object Tracking in Video Sequences

In this paper, a sequential Monte Carlo version of a data association scheme is designed to track mul- tiple targets and the track management is handled by existence probabilities calculated from the data association stage. This proposed scheme is simple and does not demand high computational resources. Tracking is based on multiple independent particle filters and the Probabilistic Data Association (PDA) algorithm which handles the uncertainty due to the measurement origin. The data association algorithm also helps to recover from full or partial occlusions. The proposed algorithm can estimate the number of active targets in the video sequence and can accordingly stop unwanted tracking filter(s).
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Data Association Analysis In Simultaneous Localization And Mapping Problem

Data Association Analysis In Simultaneous Localization And Mapping Problem

It is the objective of this paper to analyze the EKF and H∞ Filter performance considering its data association issue from the state covariance behavior(16-20). Both techniques are modified about its updated state covariance to understand how the estimations behaves. A number of papers have been recognized this issue but none of them relates the analysis to the state covariance behavior. Two distinctive noise types; gaussian and non-gaussian noises are taken into account for analysis purposes.

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Fuzzy probabilistic data association filter and its application to single maneuvering target

Fuzzy probabilistic data association filter and its application to single maneuvering target

Based on the above analysis, the fuzzy recursive least squares-probabilistic data association (FRLS-PDA) filter is proposed for single MTT in cluttered situations with unknown process noises. In the proposed filter, the PDA algorithm is applied to calculate the probabilities of the valid measurements belonging to the target. Then these probabilities are utilized to weight these measurements for constructing a fused measurement. According to the fused measurement, the measurement residual and heading change at current time are calculated, which act as the inputs of the fuzzy system designed while its out- put acts as the fuzzy fading factor of the FRLS filter. Hence, the proposed filter can estimate the current state of a maneuvering target through utilizing the fuzzy fading factor to adjust the influence of its predicted innovation on the predicted estimate. Furthermore, we analyze the adjustment function of the fuzzy fading factor on the predicted innovation for the current predicted state and then compare the calculation complexity of the proposed filter with those of the other three filters.
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A Survey on Data Association Methods in VSLAM

A Survey on Data Association Methods in VSLAM

In data association phase the newly extracted features will be mapped with the existing features and uses them to correct both the localization of robot and the landmarks. When the odometry changes as the robot moves to new position it is updated in the kalman filter through odometry update phase, which is a repetitive process. The Kalman filter is the heart of the SLAM process. It is responsible for updating where the robot thinks is based on these features. In visual SLAM data association is performed by means of visual place recognition techniques. It is categorized into following cases:
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Collaborative Perception From Data Association To Localization

Collaborative Perception From Data Association To Localization

First, we proposed a distributed optimization method for solving the permutation synchro- nization problem and a distributed optimization approach for the general case of multiway matching. We rigorously analyzed the convergence properties of the proposed algorithms and provided experimental evidence supporting that the proposed approaches, albeit decen- tralized, have performance comparable with the state of the art centralized approaches. A potential future direction consists of enforcing cycle consistency in neural networks, a direc- tion which has recently gained attention [154, 55, 105]. Another potential future direction consists of applying the proposed methods for data association in a multi-robot semantic Simultaneous Localization and Mapping (SLAM) setting.
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Optimization of Association Rules for Students' Data: A Soft Set

Optimization of Association Rules for Students' Data: A Soft Set

confidence is defined as the probability of generating strong rules if it meets some minimum threshold value. Association is to identify the association relationships or correlations in a set of items of transactions. Association analysis is widely used in any type of data which is available in real environment for decision making processes. Now days, many educational institutions are implementing this technique, directly or indirectly, to give a quality education for the students. Due to the lack of knowledge and useful information on the part of the management about students’, it is difficult to identify the students who are not achieving the standard and qualitative objective. Data mining methods includes a great deal of assignments that can be utilized proficiently to examine the exhibition and accomplishments of the understudies. Because of expanding rivalry the understudies are required to be evaluated on various parameters for which data mining methods can be convenient to actualize. Instructive Data Mining is a rising field which identifies with growing new techniques to break down data on understudies and staff data. Association analysis can be used to study this types of concepts. In this present paper, the students' data onto higher education environment is studied and analysed. Educational information mining, which concentrates valuable, already obscure examples of educational databases to improve and upgrade the learner’s presentation and improve educational quality.
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IJCSMC, Vol. 4, Issue. 5, May 2015, pg.349 – 357 RESEARCH ARTICLE A Research on Privacy Preserving Data Mining Using Heuristic Approach

IJCSMC, Vol. 4, Issue. 5, May 2015, pg.349 – 357 RESEARCH ARTICLE A Research on Privacy Preserving Data Mining Using Heuristic Approach

Abstract— Data mining is the process of identifying patterns from large amount of data. Association rule mining aims to discover dependency relationships across attributes. It may also disclose sensitive information. With extensive application of data mining techniques to various domains, privacy preservation becomes mandatory. It has become a very important area of concern but still this branch of research is in its infancy .People today have become well aware of the privacy intrusions of their sensitive data and are very reluctant to share their information. Association rule hiding is one of the techniques of privacy preserving data mining to protect the sensitive association rules generated by association rule mining. There are many approaches to hide association rule. In this paper Efficient Heuristic approach method is proposed which is more effective to hide association rule. This paper adopts heuristic approach for hiding sensitive association rules. The proposed technique makes the representative rules and hides the sensitive rules. The objective of this algorithm is to extract relevant knowledge from large amount of data, while protecting at the time sensitive information. In this paper we also focused to hide multiple sensitive items without affecting other sensitive items.
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On efficient and effective association rule mining from XML data

On efficient and effective association rule mining from XML data

The fast-growing amount of XML-based information on the web has made it desirable to develop new techniques to discover patterns and knowledge from the XML data. Association Rule (AR) mining is frequently used in data mining to reveal interesting trends, patterns, and rules in large datasets. Mining ARs from XML documents thus has become a new and interesting research topic. Association rules have been first introduced in the context of retail transaction databases [1]. Formally, an AR is an implication in the form of X→Y, where X and Y are sets of items in database D that satisfy X ∩ Y= Φ , where D is a set of data cases. X and Y are called the antecedent and subsequent of the rule, respectively. The rule X→Y has support of s in D if s% of the data cases in D contain both X and Y, and the rule has a confidence of c in D if c% of the data cases contain Y if they also contain X. Association rule mining aims to discover all large rules whose support and confidence exceed user-specified minimum support and confidence thresholds: minsup and minconf.
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Mega2: validated data-reformatting for linkage and association analyses

Mega2: validated data-reformatting for linkage and association analyses

When carrying out quality control and statistical analyses for a genetic study of a human disease, one quickly dis- covers that data organization and analysis set-up is a crit- ical, time-consuming, and extremely tedious task. Furthermore, one often needs to use several different ana- lysis programs, each with its own idiosyncratic input for- mat requirements. To meet these needs, we developed Mega2, taking the time to carefully understand the precise (sometimes poorly documented) requirements of each tar- get format, implementing our data reformatting pipeline in tested and well-documented code. Mega2’s tested and validated data conversion options expands the universe of possible analyses for the average researcher by removing the hurdle of having to tediously write, check, debug, and maintain their own conversion scripts.
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Secure Extraction of Association Rules in Parallel
Disseminated Data

Secure Extraction of Association Rules in Parallel Disseminated Data

can be. [7] As part of our discussion here is that we solve the problem, another set of secure multi-party computation problem of inclusion; Namely, the problem of where to set some ground Alice holds a private subset, and Bob ground holds an element in the set, and to determine whether they are within the subset Alice Bob elements want any of them to another party investments without revealing the information described above for inclusion beyond. The module holder applying for the loan. He / she account number, bank details like the name of the branch are and we also have car loans, home loans, business loans, etc. are providing loan categories.; Details will be sent to bank loans. Apriori [1] is designed to operate on databases containing transactions. Apriori algorithm purpose is to find associations between different sets of data, it is sometimes as "market basket analysis" is called. Each set of data and a number of items is called a transaction. Apriori's production rules that tell us how often the items are contained in the data set is set.
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196902 pdf

196902 pdf

Associatiori for Educ:ational Data Systems Data: Pro,c:essing ~ianagemellt Association Electronic Industries Association, Internationdl Comr;}unications Association Iristitute of Electri[r]

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Statistics review 8: Qualitative data – tests of association

Statistics review 8: Qualitative data – tests of association

two categorical variables. However, unlike the correlation coefficient between two quantitative variables (see Statistics review 7 [1]), it does not in itself give an indication of the strength of the association. In order to describe the associa- tion more fully, it is necessary to identify the cells that have large differences between the observed and expected fre- quencies. These differences are referred to as residuals, and they can be standardized and adjusted to follow a Normal dis- tribution with mean 0 and standard deviation 1 [2]. The adjusted standardized residuals, d ij , are given by:
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Formation of Association Rules on Hubness Using Data Mining

Formation of Association Rules on Hubness Using Data Mining

The main obstacle of FP growth is, it generates a huge number of conditional FP tree. In [3] proposed work focuses on a new and improved FP tree with a table and a new algorithm for mining association rules. Without generating the conditional FP tree the proposed algorithm mines all possible frequent item set. It also provides the frequency of frequent items, which is used to evaluate the desired association rules. In proposed FP-Tree consists of mainly two elements- the tree and a table. The tree represents the link among the items more specifically and table is used to store the spare items. We called it spare table which has two columnitem_name and frequency. Item_name is the name of the items and frequency means how many times it occurs in Stable. The main reason to discover the spare table is, in traditional FPtree a lot of branches are created and the same item appears in more than one node.
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Exact association test for small size sequencing data

Exact association test for small size sequencing data

As shown in Fig. 7, EXAT p -values differed from those of other methods, mainly because EXAT and other methods use different types of test statistics for detecting significant genes from NGS data. Our pro- posed EXAT uses the GCMH statistic for an array of contingency tables generated by the number of minor alleles and SNVs. Under the assumption of random- ness within each group, EXAT is derived under a hypergeometric distributional assumption, conditioned by marginal totals. Thus, the ratio of two GCMHs, from case and control groups, is then used to com- pare the extent of partial association between case and control groups, and the p-values are obtained by permutation tests. On the other hand, burden tests aggregate information from all rare variants in a spe- cific genomic region into a single summary variable, and obtain p -values through the chi-square distribu- tion or Hoteling ’ s t -test. SKAT is based on a regres- sion model, using a variance-component test to evaluate the significance of specific genes, using score test statistics, which follow the asymptotic chi-square distribution, under the null hypothesis.
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CNVassoc: Association analysis of CNV data using R

CNVassoc: Association analysis of CNV data using R

The proportion of variation in risk of complex diseases explained by the single nucleotide polymorphisms (SNPs) that have been discovered in recent years using the gen- ome-wide association approach appears to limited. This has lead to the suggestion that other, possibly more com- plex, genetic variants could partly explain the remaining disease susceptibility. Technological advances now allow a class of genetic variants known as copy number variants (CNV) to be genotyped with increasing levels of accuracy, and several studies have recently explored the relationship between these variants and risk of complex disease [1,2]. Genotyping these kinds of complex genetic markers is still a challenge and current laboratory techniques and plat- forms often contain a non-negligible percentage of errors. In order to minimise bias in the results of association
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Association Rule Mining for Accident Record
          Data in Mines

Association Rule Mining for Accident Record Data in Mines

Abstract — Association Rule Mining is one of the important and well accepted application areas in the field of Data Mining where rules are found between the data items which helps to determine the relationships between the data items. Thus helps to make the prediction for future in a better way. This study examined the group differences in accident susceptibility among underground coal mine workers accounting for different factors associated with them. Data was analysed with a bivariate modelling technique in the initial phase. Knowledge discovery in Database model- A Data mining technique was used for this purpose to take multiple parameters into account simultaneously. Data were collected from eight different underground coal mines for a period of ten years. The case study results revealed that different age of workers bear no significant differences in their accident susceptibility; however, the type of mines and designation show significant differences in their risk of injuries. It is inferred based on the Data Mining technique that among the
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Efficient Data Mining in SAMS through Association Rule

Efficient Data Mining in SAMS through Association Rule

burger. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements. In addition to the above example from market basket analysis association rules are employed today in many application areas including Web usage mining, intrusion detection and bioinformatics. Three parallel algorithms for mining association rules [12], an important data mining problem is formulated in this paper. These algorithms have been designed to investigate and understand the performance implications of a spectrum of trade-offs between computation, communication, memory usage, synchronization, and the use of problem-specific information in parallel data mining [13]. Fast Distributed Mining of association rules, which generates a small number of candidate sets and substantially reduces the number of messages to be passed at mining association rules [14].
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Spatial Multidimensional Association Rules Mining in Forest Fire Data

Spatial Multidimensional Association Rules Mining in Forest Fire Data

In this study, a technique in data mining, namely asso- ciation rule mining, is applied to a case study. The pur- pose of the case study is to discover relations between hot- spots occurrence and the characteristics of neighboring objects of hotspots. Pre-processing steps for spatial data were performed to prepare a dataset as the input of well- known association rule algorithm, i.e. Apriori. The results are spatial association rules describing frequent co-oc- currences between variables in the spatial database.

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Efficiency of multiple imputation to test for association in the presence of missing data

Efficiency of multiple imputation to test for association in the presence of missing data

The presence of missing data in association studies is an important problem, particularly with high- density single-nucleotide polymorphism (SNP) maps, because the probability that at least one genotype is missing dramatically increases with the number of markers. A possible strategy is to simply ignore the missing data and only use the complete observations, and, consequently, to accept a significant decrease of the sample size. Using Genetic Analysis Workshop 15 simulated data on which we removed some genotypes to generate different levels of missing data, we show that this strategy might lead to an important loss in power to detect association, but may also result in false conclusions regarding the most likely susceptibility site if another marker is in linkage disequilibrium with the disease susceptibility site. We propose a multiple imputation approach to deal with missing data on case-parent trios and evaluated the performance of this approach on the same simulated data. We found that our multiple imputation approach has high power to detect association with the susceptibility site even with a large amount of missing data, and can identify the susceptibility sites among a set of sites in linkage disequilibrium.
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Comparing the power of family based association tests for sequence data with applications in the GAW18 simulated data

Comparing the power of family based association tests for sequence data with applications in the GAW18 simulated data

We apply a family-based extension of the sequence kernel association test (SKAT) to 93 trios extracted from the 20 pedigrees in the Genetic Analysis Workshop 18 simulated data. Each extracted trio includes a unique set of parents to ensure conditionally independent trios are sampled. We compare the empirical type I error and power between the family-based SKAT and the burden test under varying percentages of causal single-nucleotide polymorphisms included in the analysis. Our investigation using simulated data suggests that, under the setting used for Genetic Analysis Workshop 18 data, both the family-based SKAT and the burden test have limited power, and that there is no substantial impact of percentage of signal on the power of either test. The low power is partially a result of the small sample size. However, we find that both the family-based SKAT and the burden test are more powerful when we use only rare variants, rather than common variants, to test the association.
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