Incremental Clustering Approach Research Articles

Privacy preserved incremental record linkage

Using an incremental approach to solve the record linkage problem is a relatively new research area. In incremental record linkage, every inserted record is compared with some existing clusters of records based on its blocking key value. Then, considering similarity, either the record will be put into an existing cluster, or a new cluster will be created for it. Although few papers have presented their solutions for incremental record linkage targeting the linkage quality or efficiency, privacy issue regarding the approach has not yet been discussed. Privacy is a major concern when record linkage is performed for sensitive data, e.g., health records, financial records, etc. In this regard, we have come up with a novel concept privacy-preserving incremental record linkage (PPiRL) which encapsulates privacy-preserving techniques with an incremental record linkage approach. In this chapter, we have proposed an end-to-end framework as our solution for PPiRL. For preserving privacy, we have used two types of privacy techniques namely phonetic encoding and generalization. We have used a recently developed phonetic algorithm “nameGist” to handle text-based features. For generalization, we have used the K-anonymization algorithm for numeric and categorical features. For handling incremental updates and internal linkage, we have used the Naive incremental clustering approach using Hierarchical Agglomerative clustering as the base clustering algorithm. We have performed various experiments to test the privacy and linkage quality of PPiRL. We have compared our work with the existing incremental record linkage framework and also with existing privacy-preserved record linkage techniques. It is apparent from our results that other than a small trade-off in linkage quality, our framework works better as a combined package of privacy and linkage solutions that any existing frameworks do not yet provide.

AE-LEACH: An Incremental Clustering Approach for Reducing the Energy Consumption in WSN

In recent years, optimizing sensor nodes energy appears to be a strong element in improving the performance of the Wireless Sensor Network (WSN). In the focus of these different approaches, substantial outputs regarding energy optimization are revealed by applying clustering-based object tracking techniques. Clustering is achieved through an enhanced adaptive LEACH algorithm, named Advanced Efficient Low-Energy Adaptive Clustering Hierarchy (AE-LEACH) algorithm. The cluster head (CH) is adopted through firm descriptors like residual energy and reserve distance from the Base Station (BS) in the clustering process. The selected CH predicts the target trajectory using Particle Filter (PA) algorithm and selectively activates from the next round of sensor nodes to continuously track the target. The target nodes’ predictable locations are routed to the base station through the backbone that lies in the cluster head. Gini index is used to assess the energy efficiency of the clustering algorithm . Thus, the proposed performance metrics stated on network timespan, residual energy, energy consumption, and Gini index for remaining energy and achieves significantly lower Gini index of upto 78.38%, 86.11%, and 85.92% as compared to K means clustering , HEED clustering, and LEACH clustering algorithm, respectively. Moreover, time accuracy is directly proportional to the network lifetime and energy consumption.

Computational analysis of incremental clustering approaches for Large Data

Clustering is an approach of data mining, which helps us to find the underlying hidden structure in the dataset. K-means is a clustering method which usages distance functions to find the similarities or dissimilarities between the instances. DBSCAN is a clustering algorithm, which discovers the arbitrary shapes & sizes of clusters from huge volume of using spatial density method. These two approaches of clustering are the classical methods for efficient clustering but underperform when the data is updated frequently in the databases so, the incremental or gradual clustering approaches are always preferred in this environment. In this paper, an incremental approach for clustering is introduced using K-means and DBSCAN to handle the new datasets dynamically updated in the database in an interval.

Creating Incremental Models of Indoor Environments through Omnidirectional Imaging

In this work, an incremental clustering approach to obtain compact hierarchical models of an environment is developed and evaluated. This process is performed using an omnidirectional vision sensor as the only source of information. The method is structured in two loop closure levels. First, the Node Level Loop Closure process selects the candidate nodes with which the new image can close the loop. Second, the Image Level Loop Closure process detects the most similar image and the node with which the current image closed the loop. The algorithm is based on an incremental clustering framework and leads to a topological model where the images of each zone tend to be clustered in different nodes. In addition, the method evaluates when two nodes are similar and they can be merged in a unique node or when a group of connected images are different enough to the others and they should constitute a new node. To perform the process, omnidirectional images are described with global appearance techniques in order to obtain robust descriptors. The use of such technique in mapping and localization algorithms is less extended than local features description, so this work also evaluates the efficiency in clustering and mapping techniques. The proposed framework is tested with three different public datasets, captured by an omnidirectional vision system mounted on a robot while it traversed three different buildings. This framework is able to build the model incrementally, while the robot explores an unknown environment. Some relevant parameters of the algorithm adapt their value as the robot captures new visual information to fully exploit the features’ space, and the model is updated and/or modified as a consequence. The experimental section shows the robustness and efficiency of the method, comparing it with a batch spectral clustering algorithm.

Incremental Density-Based Clustering on Multicore Processors.

The density-based clustering algorithm is a fundamental data clustering technique with many real-world applications. However, when the database is frequently changed, how to effectively update clustering results rather than reclustering from scratch remains a challenging task. In this work, we introduce IncAnyDBC, a unique parallel incremental data clustering approach to deal with this problem. First, IncAnyDBC can process changes in bulks rather than batches like state-of-the-art methods for reducing update overheads. Second, it keeps an underlying cluster structure called the object node graph during the clustering process and uses it as a basis for incrementally updating clusters wrt. inserted or deleted objects in the database by propagating changes around affected nodes only. In additional, IncAnyDBC actively and iteratively examines the graph and chooses only a small set of most meaningful objects to produce exact clustering results of DBSCAN or to approximate results under arbitrary time constraints. This makes it more efficient than other existing methods. Third, by processing objects in blocks, IncAnyDBC can be efficiently parallelized on multicore CPUs, thus creating a work-efficient method. It runs much faster than existing techniques using one thread while still scaling well with multiple threads. Experiments are conducted on various large real datasets for demonstrating the performance of IncAnyDBC.

Incremental Learning of Object Models From Natural Human–Robot Interactions

In order to perform complex tasks in realistic human environments, robots need to be able to learn new concepts in the wild , incrementally, and through their interactions with humans. This article presents an end-to-end pipeline to learn object models incrementally during the human–robot interaction (HRI). The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models. Throughout the article, we highlight the main challenges associated with this problem, such as high degree of occlusion and clutter, domain change, low-resolution data, and interaction ambiguity. This article shows the benefits of using multiview approaches and combining visual and language features, and our experimental results outperform standard baselines. Note to Practitioners —This article was motivated by challenges in recognition tasks for dynamic and varying scenarios. Our approach learns to recognize new user interactions and objects. To do so, we use multimodal data from the user–robot interaction; visual data are used to learn the objects and speech is used to learn the label and help with the interaction-type recognition. We use state-of-the-art deep learning (DL) models to segment the user and the objects in the scene. Our algorithm for incremental learning is based on a classic incremental clustering approach. The pipeline we propose works with all sensors mounted on the robot, so it allows mobility on the system. This article uses the data recorded from a Baxter robot, which enables the use of the manipulation arms in future steps, but it would work with any robot that can have the same sensors mounted. The sensors used are two RGB-D cameras and a microphone. The pipeline currently has high computational requirements to run the two DL-based steps. We have tested it with a desktop computer, including a GTX 1060 and 32 GB of RAM.

Identification and simplification of T-S fuzzy neural networks based on incremental structure learning and similarity analysis

This paper proposes a novel identification method and a simplification scheme for T-S fuzzy neural networks, which consists of two steps. The first step refers to the structure design based on an incremental clustering approach whose basic ideas are that the structure identification of fuzzy neural networks is guided by the attenuation of output approximation error in each cluster and processed by a recursive refined clustering iteration with the input space clustering and sub-clustering as the main steps. Once the structure of a T-S fuzzy neural network is identified by the incremental clustering approach, its parameters are further learned and refined by the Levenberg-Marquardt optimization algorithm. The second step refers to the structure simplification including removing redundant fuzzy rules and merging highly similar fuzzy rules. Furthermore, the performances of several similarity calculating methods are analyzed and discussed, which provides a basis for the selection of the appropriate similarity analysis and effective calculating method for the merging of fuzzy rules and system simplification. That is, the given performance analysis provides the methodology basis and design guide for structure simplification based on similarity analysis and merger of fuzzy rules. Several experiments are implemented to illustrate the feasibility and effectiveness of the proposed approach.

A bibliometric survey on incremental clustering algorithm for electricity smart meter data analysis

Smart meters (SMs) are an electronic device for recording customer energy consumption in the time intervals of an hour or less. The use of SMs incurs benefits to people in various aspects such as environmental, social, and economical. SMs frequently communicate with utility companies for monitoring and management of energy usage as well as with customers for observing their energy consumption. It generates a considerable amount of electricity smart meter data incrementally. In the clustering task, instead of re-clustering all data from scratch on the influx of new data, it is better to update clustering result incrementally based on new as well as old data. Thus, an incremental clustering approach is an essential way to overcome the issue related to clustering with growing data. The purpose of the paper is to dig out all the researches in smart meter data analytics and incremental clustering to make the concept clear for future researchers. This bibliometric analysis is implemented using the repositories such as Scopus, Google Scholar, ResearchGate, and the tools like Gephi, Table2Net, and GPS Visualizer, etc. The study revealed that the maximum number of the reviews on smart meter and incremental clustering had explored very recently.

Locally convex-regions approximation using an incremental quadratic-based fuzzy clustering

Choosing an appropriate local optimal region in order to satisfy the location priorities and to guarantee enough robustness against measurement biases is desired in many optimization problems. To fulfill such aim, all locally convex regions which potentially contain optimal points must be approximated. In this paper, using a quadratic-based fuzzy clustering, approximation of locally convex regions of Multiple-Convex Functions (MCFs) is intended. At first, using an incremental fuzzy clustering approach, the input space is partitioned as hyper-rectangle regions in which Locally Quadratic Models (LQMs) are identified. Based on the Hessian matrices of LQMs, some clusters, that potentially contain convex regions, are chosen. Around a certain patch of each chosen cluster, a high-order model is fitted, through which a Gradient-based Ordinal Differential Equation (GODE) is defined. Estimating the domain of attraction of each defined GODE, a locally convex region is approximated. Then, robustness of the approximated convex regions, against unknown bounded biases of input variables, is discussed. A theorem is stated which conservatively determines the sub-regions remaining convex even in presence of the uncertainty. To explain the methodology of the proposed method, an illustrative example is given. Then, the suggested method is applied to the power economic dispatch (PED) problem. The achieved results demonstrate the capability of the proposed method.

ICGT: A novel incremental clustering approach based on GMM tree

Streaming data presents new challenges to data mining algorithms. To conduct data clustering on the streaming data, this paper proposes a novel incremental clustering approach utilizing Gaussian Mixture Model (GMM), termed as ICGT (Incremental Construction of GMM Tree). The ICGT creates and dynamically adjusts a GMM tree consistent to the sequentially presented data. Each leaf node in the tree corresponds to a dense Gaussian distribution and each non-leaf node to a GMM. To update the GMM tree for insertion of the newly arrived data points, we introduce the definitions of node connectivity and connected subsets, and present the tree update algorithm. We further develop a clustering evaluation criterion and search strategy to determine the final partition of the data set based on the constructed GMM tree. We evaluated the proposed approach on synthetic and real-world data sets and compared ICGT with other incremental and static clustering methods. The experimental results confirm that our approach is effective and promising.

An incremental clustering method based on the boundary profile.

Many important applications continuously generate data, such as financial transaction administration, satellite monitoring, network flow monitoring, and web information processing. The data mining results are always evolving with the newly generated data. Obviously, for the clustering task, it is better to incrementally update the new clustering results based on the old data rather than to recluster all of the data from scratch. The incremental clustering approach is an essential way to solve the problem of clustering with growing Big Data. This paper proposes a boundary-profile-based incremental clustering (BPIC) method to find arbitrarily shaped clusters with dynamically growing datasets. This method represents the existing clustering results with a collection of boundary profiles and discards the inner points of clusters rather than keep all data. It greatly saves both time and space storage costs. To identify the boundary profile, this paper presents a boundary-vector-based boundary point detection (BV-BPD) algorithm that summarizes the structure of the existing clusters. The BPIC method processes each new point in an online fashion and updates the clustering results in a batch mode. When a new point arrives, the BPIC method either immediately labels it or temporarily puts it into a bucket according to the relationship between the new data and the boundary profiles. A bucket is employed to distinguish the noise from the potential seeds of new clusters and alleviate the effects of data order. When the bucket is full, the BPIC method will cluster the data within it and update the clustering results. Thus, the BPIC method is insensitive to noise and the order of new data, which is critical for the robustness of the incremental clustering process. In the experiments, the performance of the boundary point detection algorithm BV-BPD is compared with the state-of-the-art method. The results show that the BV-BPD is better than the state-of-the-art method. Additionally, the performance of BPIC and other two incremental clustering methods are investigated in terms of clustering quality, time and space efficiency. The experimental results indicate that the BPIC method is able to get a qualified clustering result on a large dataset with higher time and space efficiency.

Real-time event detection from the Twitter data stream using the TwitterNews+ Framework

Detecting events in real-time from the Twitter data stream has gained substantial attention in recent years from researchers around the world. Different event detection approaches have been proposed as a result of these research efforts. One of the major challenges faced in this context is the high computational cost associated with event detection in real-time. We propose, TwitterNews+, an event detection system that incorporates specialized inverted indices and an incremental clustering approach to provide a low computational cost solution to detect both major and minor newsworthy events in real-time from the Twitter data stream. In addition, we conduct an extensive parameter sensitivity analysis to fine-tune the parameters used in TwitterNews+ to achieve the best performance. Finally, we evaluate the effectiveness of our system using a publicly available corpus as a benchmark dataset. The results of the evaluation show a significant improvement in terms of recall and precision over five state-of-the-art baselines we have used.

Configuration-Based Fingerprinting of Mobile Device Using Incremental Clustering

Device fingerprinting has lately received great attention due to its effectiveness in fraud detection, secure authentication, and user tracking. Whereas fingerprinting performs well on labeled device data using classification methods, there are several researches concentrated on unlabeled mobile device data and existing methods often lack in precision and efficiency. To overcome this challenge, we focus on the use of the mobile device’s configuration-related characteristics as a mean to build a device fingerprint, which allows to distinctively and reliably characterize each device. In addition, an incremental clustering approach is proposed to classify unlabeled device data into clusters on the basis of their similarity. Moreover, we customize individual distance threshold for each user according to their device configurations’ modifying frequency, in order to construct a precise authentication mechanism between users and devices. The proposed clustering model and device fingerprinting system are evaluated on 8220 fingerprints from 815 different devices. The experimental results demonstrate the effectiveness and efficiency of our algorithms.

A bio-inspired, incremental clustering algorithm for semantics-based web service discovery

Web service discovery is a challenging task due to the widespread availability of published services on the web. In this paper, a service crawler-based web service discovery framework is proposed, that employs information retrieval techniques to effectively retrieve available, published service descriptions. Their functional semantics is extracted for similarity computation and tag generation using natural language processing techniques. The framework is inherently dynamic in nature as new service descriptions may be continually added during periodic crawler runs or existing ones may be removed if service is unavailable. To deal with these issues, a dynamic, incremental clustering approach based on bird flocking behaviour is proposed. Experimental results show that semantic analysis and automatic tagging captured the services' functional semantics in a meaningful way. The algorithm effectively handled the dynamic requirements of the proposed framework by eliminating cluster recomputation overhead and achieved a speed-up factor of 61.8% when compared to hierarchical clustering.

Incremental Fuzzy Clustering With Multiple Medoids for Large Data

As an important technique of data analysis, clustering plays an important role in finding the underlying pattern structure embedded in unlabeled data. Clustering algorithms that need to store all the data into the memory for analysis become infeasible when the dataset is too large to be stored. To handle such large data, incremental clustering approaches are proposed. The key idea behind these approaches is to find representatives (centroids or medoids) to represent each cluster in each data chunk, which is a packet of the data, and final data analysis is carried out based on those identified representatives from all the chunks. In this paper, we propose a new incremental clustering approach called incremental multiple medoids-based fuzzy clustering (IMMFC) to handle complex patterns that are not compact and well separated. We would like to investigate whether IMMFC is a good alternative to capturing the underlying data structure more accurately. IMMFC not only facilitates the selection of multiple medoids for each cluster in a data chunk, but also has the mechanism to make use of relationships among those identified medoids as side information to help the final data clustering process. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IMMFC are provided. Experimental studies on several large datasets that include real world malware datasets have been conducted. IMMFC outperforms existing incremental fuzzy clustering approaches in terms of clustering accuracy and robustness to the order of data. These results demonstrate the great potential of IMMFC for large-data analysis.

A novel incremental conceptual hierarchical text clustering method using CFu-tree

As a data mining method, clustering, which is one of the most important tools in information retrieval, organizes data based on unsupervised learning which means that it does not require any training data. But, some text clustering algorithms cannot update existing clusters incrementally and, instead, have to recompute a new clustering from scratch. In view of above, this paper presents a novel down-top incremental conceptual hierarchical text clustering approach using CFu-tree (ICHTC-CF) representation, which starts with each item as a separate cluster. Term-based feature extraction is used for summarizing a cluster in the process. The Comparison Variation measure criterion is also adopted for judging whether the closest pair of clusters can be merged or a previous cluster can be split. And, our incremental clustering method is not sensitive to the input data order. Experimental results show that the performance of our method outperforms k-means, CLIQUE, single linkage clustering and complete linkage clustering, which indicate our new technique is efficient and feasible.

Incremental kernel spectral clustering for online learning of non-stationary data

In this work a new model for online clustering named Incremental kernel spectral clustering (IKSC) is presented. It is based on kernel spectral clustering (KSC), a model designed in the Least Squares Support Vector Machines (LS-SVMs) framework, with primal-dual setting. The IKSC model is developed to quickly adapt itself to a changing environment, in order to learn evolving clusters with high accuracy. In contrast with other existing incremental spectral clustering approaches, the eigen-updating is performed in a model-based manner, by exploiting one of the Karush–Kuhn–Tucker (KKT) optimality conditions of the KSC problem. We test the capacities of IKSC with some experiments conducted on computer-generated data and a real-world data-set of PM10 concentrations registered during a pollution episode occurred in Northern Europe in January 2010. We observe that our model is able to precisely recognize the dynamics of shifting patterns in a non-stationary context.

Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering

Extracting meaningful information from the growing quantity of spatial data is a challenge. The issues are particularly evident with spatio-temporal data describing movement. Such data typically corresponds to movement of humans, animals and machines in the physical environment. This article considers a special form of movement data generated through human–computer interactions with online web maps. As a user interacts with a web map using a mouse as a pointing tool, invisible trajectories are generated. By examining the spatial features on the map where the mouse cursor visits, a user's interests and experience can be detected. To analyse this valuable information, we have developed a geovisual analysis tool which provides a rich insight into such user behaviour. The focus of this paper is on a clustering technique which we apply to mouse trajectories to group trajectories with similar behavioural properties. Our experiments reveal that it is possible to identify experienced and novice users of web mapping environments using an incremental clustering approach. The results can be used to provide personalised map interfaces to users and provide appropriate interventions for completing spatial tasks.

An Efficient Incremental Clustering based Summarization Technique for Web Page Classification

Currently the World Wide Web is the largest source of information. There are numerous self-acting classification advances that have been suggested. In this suggested work an effective incremental clustering approach to evolve a better incremental clustering based summarization method for world wide World Wide Web classification which can facilitate to better coordinate the accessible data on WWW. The incremental clustering founded summarization technique permits dynamic tracking of the ever expanding allotment of information, put on the World Wide Web every day. This is the useful procedure for dynamic contents. In this the clustering of web document is a key method for finding out a more contextual and noiseless knowledge for world wide world wide web utilizes. C4.5 is one of the most classic classification algorithms on data excavation, when it is utilized in mass computed results, the effectiveness is very low. In this paper, the direct of C4.5 is advanced by the use of L’Hospital direct, which simplifies the assessment process and advances the effectiveness of decision making algorithm. The aim of this work to apply the algorithms in a very time and space effective kind and throughput and answer time for the application will be encouraged as the presentation measures. The aspires is to implement these algorithms and graphically compared the complexities and efficiencies of the classification algorithms.

New Proximity Estimate for Incremental Update of Non-Uniformly Distributed Clusters

The conventional clustering algorithms mine static databases and generate a set of patterns in the form of clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns extracted from the original database become obsolete. Thus the conventional clustering algorithms are not suitable for incremental databases due to lack of capability to modify the clustering results in accordance with recent updates. In this paper, the author proposes a new incremental clustering algorithm called CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity. CFICA makes use of the proposed proximity metric to determine the membership of a data point into a cluster.