System: Based on the search results given, can you provide a concise overview of the main hypothesis or research question addressed in the article, along with the key methodologies used by the researchers to investigate it? Additionally, could you summarize the main findings obtained from the experiments and discuss their implications for advancing state-of-the-art research in the field? Lastly, please identify any limitations of the study and suggest potential areas for future research, while also evaluating the strengths and weaknesses of the presented conclusions. The answer should be well structured and between 300 and 400 words.

User: SEARCH RESULTS:

[Page no. 2] "training multiple autoencoders of different sizes. The intuition behind this choice is that autoencoders at multiple resolutions capture better the multifaceted diversity relationships among attributes during the clustering process. We assess the effec- tiveness of our framework by comparing its performances to several competitors’ ones. In our experimental study, we show that our algorithm outperforms state-of-the-art semi- supervised clustering techniques, whether they are based on pairwise constraints, on metric-learning or on both approaches. The remainder of the paper is organized as follows: Sec- tion II presents some closely related works; Section III in- troduces the theoretical foundations of our framework; we describe our semi-supervised clustering method in Section IV and report the experimental results in Section V; ﬁnally, Section VI concludes and provides some ideas for future research directions. 978-1-5090-6014-6/18/$31.00 ©2018 IEEE  II. RELATED WORK Semi-supervised clustering is a ﬁfteen years old albeit still very active research ﬁeld (see, e.g., [4] for a state-of- the-art survey). It supports classiﬁcation tasks when labeled data are limited and/or expensive to collect. As such, it has been mainly studied in the context of semi-supervised learning where two alternative classes of methods have been studied: metric-based methods, consisting in learning a metric considering labeled data before applying standard clustering, and constraint-based methods, which force pairwise (e.g., must-link and cannot-link) constraints satisfaction during the clustering process. A solution is to use the knowledge provided by the few available labeled instances within a clustering algo- rithm. In [26], a simple adaptation of k-means which enforces must-link and cannot-link constraints during the clustering process is described. [2] proposes a constrained clustering approach that leverages labeled data during the initialization and clustering steps. An example of metric-based approach is given in [16]. Instead, [5] integrates both constraint-based and metric-based approaches in a k-means-like algorithm. In [3], the"

[Page no. 6] "constraints (see, for instance, MPCKmeans in Figure 2(b) and PCKmeans in Figure 2(c)). As second general remark, we observe that our approach is the only one that always outperforms the fully unsupervised baseline (the k-means algorithm). This result stresses the abil- ity of MSAEClust to systematically leverage the background information w.r.t. a fully unsupervised clustering process. We point out that this is not the case for any of the other methods considered in this study: ITML only outperforms the baseline on Spambase data; PCKmeans fails to perform better than baseline on Mushroom; MPCKmeans has issues regarding the results on USPS; ﬁnally, SeededKmeans has the same behavior of its fully unsupervised counterpart on three datasets over six (Landsat, Mushroom and Letter). To better understand how much each method complies with the injected background knowledge, we compute the NMI by only considering the labeled examples in an experiment conducted similarly to the previous one. Logically, for this analysis, we discard the k-means algorithm. The results are reported in Figure 3. The behavior we obtain is coherent with the results we have observed in the previous compar- ison. Generally, the more the algorithm ﬁts the background knowledge, the more it achieves good performances on the whole benchmark data. The only exception concerns dataset Spambase. In this case, MSAEClust is the method that best ﬁts the background knowledge but, overall, the best perfor- mances on the whole dataset are obtained by MPCKmeans (Figure 2(d)). As explained before, this is not unusual in constraint-based clustering, and it is even more noticeable by observing Figure 3: higher quantities of background knowl- edge do not guarantee better performances on the data. This is not unexpected. In semi-supervised clustering, it is well- known that the quality of the background knowledge is more important than its quantity [8]. Fig."

[Page no. 1] "Semi-Supervised Clustering with Multiresolution Autoencoders Dino Ienco TETIS, IRSTEA, Univ. Montpellier LIRMM Montpellier, France <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> Ruggero G. Pensa Department of Computer Science University of Turin Turin, Italy <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> Abstract—In most real world clustering scenarios, experts generally dispose of limited background information, but such knowledge is valuable and may guide the analysis process. Semi-supervised clustering can be used to drive the algorithmic process with prior knowledge and to enable the discovery of clusters that meet the analyst’s expectations. Usually, in the semi-supervised clustering setting, the background knowledge is converted to some kind of constraint and, successively, metric learning or constrained clustering are adopted to obtain the ﬁnal data partition. Conversely, we propose a new semi-supervised clustering algorithm that directly exploits prior knowledge, under the form of labeled examples, avoiding the necessity to derive constraints. Our algorithm employs a multiresolution strategy to generate an ensemble of semi-supervised autoencoders that ﬁt the data together with the background knowledge. Successively, the network models are employed to supply a new embedding representation on which clustering is performed. The proposed strategy is evaluated on a set of real-world benchmarks also in comparison with well-known state-of-the-art semi-supervised clustering methods. The experimental results highlight the beneﬁt of directly leveraging the prior knowledge and show the quality of the representation learnt by the multiresolution schema. Index Terms—semi-supervised clustering, background knowl- edge, autoencoders, ensemble I. INTRODUCTION Clustering is by far one of the most popular machine learning techniques due to the wide range of unsupervised application settings [14]. Although unsupervised problems are very common, analysts/data scientists are often unsatisﬁed of clustering algorithms, since their expectation is frequently violated by the results. Indeed, some (limited) knowledge about the data is likely to be owned by the expert who may know the expected cluster structure of few samples of interest. Semi-supervised clustering"

[Page no. 5] "the behavior and the performances of our algorithm. First, we compare MSAEClust to other semi- supervised clustering methods. Then, we perform an in-depth analysis of MSAEClust’s results to assess its sensitivity w.r.t. to the ensemble size. Finally, we investigate qualitatively the new embedding generated by our algorithm through visual inspection. A. Competitors For the comparative study, we consider the following com- petitors: Information Theoretic metric learning (ITML), pair- wise constrained k-means (PCKmeans), metric-pairwise con- strained k-means (MPCKmeans), and seeded k-means (Seed- edKmeans). ITML exploits information theory to learn a suit- able metric space that meets the supervision supplied as side- information on the distance function [9]. Since it only supplies a new metric space, we use k-means to successively obtain the data partition. PCKmeans leverages must-link and cannot- link constraints to modify the internal objective function [3]. MPCKmeans combines metric-learning and constrained clus- tering to exploit as much as possible the supplied supervi- sion [5]. SeededKmeans uses labeled instances to initialize a standard k-means clustering algorithm [2]. Finally, we also include k-means in the comparative study, so as to have a simple fully unsupervised baseline. B. Datasets To evaluate the comparative and in-depth analysis results, the experiments are performed over six publicly available datasets published in the UCI machine learning repository1. TABLE I MAIN CHARACTERISTICS OF THE DATASETS Dataset # Instances # Features # Classes Splice 3190 60 3 Landsat 4 435 36 6 Mushroom 8 124 22 2 Spambase 4 601 57 2 Letter 20 000 16 26 USPS 9 298 256 10 1http://archive.ics.uci.edu/ml/index.php 2018 International Joint Conference on Neural Networks (IJCNN)  (a) Splice (b) Landsat (c) Mushroom (d) Spambase (e) Letter (f) USPS Fig. 2. NMI values of the comparative study (best viewed in color version, available in electronic format). A summary of the information about the"

[Page no. 8] "have introduced MSAEClust, a semi- supervised clustering algorithm that directly exploits labeled examples to produce the ﬁnal data partitioning. Our approach leverages an ensemble of semi-supervised autoencoder to produce a new representation space that is successively used to perform clustering. The ensemble construction leverages a multiresolution strategy that enables the injection of di- versity in the set of network models generated. Extensive experimentations in comparison with state-of-the-art semi- supervised clustering algorithms have shown the beneﬁt of the framework proposed in this paper. As future work we will study how to couple the embedding learnt by our framework with constrained clustering algorithms in order to exploit the background knowledge at both levels of the semi-supervised clustering process. VII. ACKNOWLEDGMENTS The authors acknowledge the support of the National Re- search Agency within the framework of the program ”In- vestissements d’Avenir” for the GEOSUD project (ANR-10- EQPX-20). REFERENCES [1] A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, “Clustering with bregman divergences,” Journal of Machine Learning Research, vol. 6, pp. 1705–1749, 2005. [2] S. Basu, A. Banerjee, and R. J. Mooney, “Semi-supervised clustering by seeding,” in Proceedings ICML 2002, Sydney, Australia, 2002, pp. 27–34. [3] S. Basu, M. Bilenko, and R. J. Mooney, “A probabilistic framework for semi-supervised clustering,” in Proceedings ACM SIGKDD 2004, Seattle, USA, 2004, pp. 59–68. [4] S. Basu, I. Davidson, and K. W. (Editors), Constrained Clustering: Advances in Algorithms, Theory and Applications. Chapman & Hall/CRC Press, Data Mining and Knowledge Discovery Series, 2008. [5] M. Bilenko, S. Basu, and R. J. Mooney, “Integrating constraints and metric learning in semi-supervised clustering.” in Proceedings ICML 2004, Banff, Canada, 2004, pp. 81–88. [6] J. Chen, S. Sathe, C. C. Aggarwal, and D. S. Turaga, “Outlier detection with autoencoder ensembles,” in SDM, 2017, pp. 90–98. [7] M. Cucuringu, I. Koutis, S. Chawla,"

SUMMARY:
Assistant: